{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Example: Attention-based Deep Multiple Instance Learning</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import general modules used for e.g. plotting.\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import sys\n",
    "import torch\n",
    "\n",
    "# Import Hopfield-specific modules.\n",
    "from modules import HopfieldPooling\n",
    "\n",
    "# Import auxiliary modules.\n",
    "from distutils.version import LooseVersion\n",
    "from typing import Optional, Tuple\n",
    "\n",
    "# Importing PyTorch specific modules.\n",
    "from torch import Tensor\n",
    "from torch.autograd import Variable\n",
    "from torch.nn import Conv2d, Dropout, Linear, MaxPool2d, Module, ReLU, Sequential, Sigmoid\n",
    "from torch.nn.utils import clip_grad_norm_\n",
    "from torch.optim import AdamW\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "# Set plotting style.\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Append the path of the Attention-based Deep Multiple Instance Learning (ADMIL) repository to the system path in order for Python to find the corresponding modules to import."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "sys.path.append(r'./AttentionDeepMIL')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Afterwards, the corresponding modules\n",
    "- <code>MnistBags</code>\n",
    "- <code>Attention</code>\n",
    "- <code>GatedAttention</code>\n",
    "\n",
    "are imported to the global namespace."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dataloader import MnistBags\n",
    "from model import Attention, GatedAttention"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Specific minimum versions of Python itself as well as of some used modules is required."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Installed Python version:  3.8.8 (✓)\n",
      "Installed PyTorch version: 1.7.0 (✓)\n"
     ]
    }
   ],
   "source": [
    "python_check = '(\\u2713)' if sys.version_info >= (3, 8) else '(\\u2717)'\n",
    "pytorch_check = '(\\u2713)' if torch.__version__ >= LooseVersion(r'1.5') else '(\\u2717)'\n",
    "\n",
    "print(f'Installed Python version:  {sys.version_info.major}.{sys.version_info.minor}.{sys.version_info.micro} {python_check}')\n",
    "print(f'Installed PyTorch version: {torch.__version__} {pytorch_check}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "<h3 style=\"color:rgb(0,120,170)\">Create Dataset</h3>\n",
    "\n",
    "The dataset itself falls into the category of <i>binary classification</i> tasks in the domain of <i>Multiple Instance Learning (MIL)</i> problems.\n",
    "The MNIST-bags task was introcuded by Ilse and Tomczak:<br>\n",
    "<cite>Ilse, M., Tomczak, J.M. and Welling, M., 2018. Attention-based deep multiple instance learning. arXiv preprint arXiv:1802.04712.</cite><br><br>\n",
    "Each bag comprises a collection of $28\\times{}28$ grayscale images/instances, whereas each instance is a sequence of pixel values in the range of $[0; 255]$. The amount of instances per pag is drawn from a Gaussian with specified mean and variance. The positive class is defined by the presence of the target number/digit, whereas the negative one by its absence. This demonstration shows, that <code>HopfieldPooling</code> is capable of learning and filtering each bag with respect to the class-defining target number/digit. Defining arguments are:\n",
    "<br><br>\n",
    "<table>\n",
    "    <tr>\n",
    "        <th>Argument</th>\n",
    "        <th>Value (used in this demo)</th>\n",
    "        <th>Description</th>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <th><code>target_number</code></th>\n",
    "        <th>9</th>\n",
    "        <th>Target number/digit defining class affiliation.</th>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <th><code>mean_bag_length</code></th>\n",
    "        <th>10</th>\n",
    "        <th>Mean amount of instances per bag.</th>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <th><code>var_bag_length</code></th>\n",
    "        <th>2</th>\n",
    "        <th>Variance of amount of instances per bag.</th>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <th><code>num_bag</code></th>\n",
    "        <th>{200; 50}</th>\n",
    "        <th>Amount of samples of the training as well as validation set.</th>\n",
    "    </tr>\n",
    "</table>\n",
    "\n",
    "Let's define the dataset using previously mentioned properties as well as a logging directory for storing all auxiliary outputs like performance plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device(r'cuda:0' if torch.cuda.is_available() else r'cpu')\n",
    "\n",
    "# Create data loader of training set.\n",
    "data_loader_train = DataLoader(MnistBags(\n",
    "    target_number=9,\n",
    "    mean_bag_length=10,\n",
    "    var_bag_length=2,\n",
    "    num_bag=200,\n",
    "    train=True\n",
    "), batch_size=1, shuffle=True)\n",
    "\n",
    "# Create data loader of validation set.\n",
    "data_loader_eval = DataLoader(MnistBags(\n",
    "    target_number=9,\n",
    "    mean_bag_length=10,\n",
    "    var_bag_length=2,\n",
    "    num_bag=50,\n",
    "    train=False\n",
    "), batch_size=1, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_dir = f'resources/'\n",
    "os.makedirs(log_dir, exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "<h3 style=\"color:rgb(0,120,170)\">Create Auxiliaries</h3>\n",
    "\n",
    "Before digging into Hopfield-based networks, a few auxiliary variables and functions need to be defined. This is nothing special with respect to Hopfield-based networks, but rather common preparation work of (almost) every machine learning setting (e.g. definition of a <i>data loader</i> as well as a <i>training loop</i>). We will see, that this comprises the most work of this whole demo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_epoch(network: Module,\n",
    "                optimiser: AdamW,\n",
    "                data_loader: DataLoader\n",
    "               ) -> Tuple[float, float, float]:\n",
    "    \"\"\"\n",
    "    Execute one training epoch.\n",
    "    \n",
    "    :param network: network instance to train\n",
    "    :param optimiser: optimiser instance responsible for updating network parameters\n",
    "    :param data_loader: data loader instance providing training data\n",
    "    :return: tuple comprising training loss, training error as well as accuracy\n",
    "    \"\"\"\n",
    "    network.train()\n",
    "    losses, errors, accuracies = [], [], []\n",
    "    for data, target in data_loader:\n",
    "        data, target = data.to(device=device), target[0].to(device=device)\n",
    "\n",
    "        # Process data by Hopfield-based network.\n",
    "        loss = network.calculate_objective(data, target)[0]\n",
    "\n",
    "        # Update network parameters.\n",
    "        optimiser.zero_grad()\n",
    "        loss.backward()\n",
    "        clip_grad_norm_(parameters=network.parameters(), max_norm=1.0, norm_type=2)\n",
    "        optimiser.step()\n",
    "\n",
    "        # Compute performance measures of current model.\n",
    "        error, prediction = network.calculate_classification_error(data, target)\n",
    "        accuracy = (prediction == target).to(dtype=torch.float32).mean()\n",
    "        accuracies.append(accuracy.detach().item())\n",
    "        errors.append(error)\n",
    "        losses.append(loss.detach().item())\n",
    "    \n",
    "    # Report progress of training procedure.\n",
    "    return sum(losses) / len(losses), sum(errors) / len(errors), sum(accuracies) / len(accuracies)\n",
    "\n",
    "\n",
    "def eval_iter(network: Module,\n",
    "              data_loader: DataLoader\n",
    "             ) -> Tuple[float, float, float]:\n",
    "    \"\"\"\n",
    "    Evaluate the current model.\n",
    "    \n",
    "    :param network: network instance to evaluate\n",
    "    :param data_loader: data loader instance providing validation data\n",
    "    :return: tuple comprising validation loss, validation error as well as accuracy\n",
    "    \"\"\"\n",
    "    network.eval()\n",
    "    with torch.no_grad():\n",
    "        losses, errors, accuracies = [], [], []\n",
    "        for data, target in data_loader:\n",
    "            data, target = data.to(device=device), target[0].to(device=device)\n",
    "\n",
    "            # Process data by Hopfield-based network.\n",
    "            loss = network.calculate_objective(data, target)[0]\n",
    "\n",
    "            # Compute performance measures of current model.\n",
    "            error, prediction = network.calculate_classification_error(data, target)\n",
    "            accuracy = (prediction == target).to(dtype=torch.float32).mean()\n",
    "            accuracies.append(accuracy.detach().item())\n",
    "            errors.append(error)\n",
    "            losses.append(loss.detach().item())\n",
    "\n",
    "        # Report progress of validation procedure.\n",
    "        return sum(losses) / len(losses), sum(errors) / len(errors), sum(accuracies) / len(accuracies)\n",
    "\n",
    "    \n",
    "def operate(network: Module,\n",
    "            optimiser: AdamW,\n",
    "            data_loader_train: DataLoader,\n",
    "            data_loader_eval: DataLoader,\n",
    "            num_epochs: int = 1\n",
    "           ) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:\n",
    "    \"\"\"\n",
    "    Train the specified network by gradient descent using backpropagation.\n",
    "    \n",
    "    :param network: network instance to train\n",
    "    :param optimiser: optimiser instance responsible for updating network parameters\n",
    "    :param data_loader_train: data loader instance providing training data\n",
    "    :param data_loader_eval: data loader instance providing validation data\n",
    "    :param num_epochs: amount of epochs to train\n",
    "    :return: data frame comprising training as well as evaluation performance\n",
    "    \"\"\"\n",
    "    losses, errors, accuracies = {r'train': [], r'eval': []}, {r'train': [], r'eval': []}, {r'train': [], r'eval': []}\n",
    "    for epoch in range(num_epochs):\n",
    "        \n",
    "        # Train network.\n",
    "        performance = train_epoch(network, optimiser, data_loader_train)\n",
    "        losses[r'train'].append(performance[0])\n",
    "        errors[r'train'].append(performance[1])\n",
    "        accuracies[r'train'].append(performance[2])\n",
    "        \n",
    "        # Evaluate current model.\n",
    "        performance = eval_iter(network, data_loader_eval)\n",
    "        losses[r'eval'].append(performance[0])\n",
    "        errors[r'eval'].append(performance[1])\n",
    "        accuracies[r'eval'].append(performance[2])\n",
    "    \n",
    "    # Report progress of training and validation procedures.\n",
    "    return pd.DataFrame(losses), pd.DataFrame(errors), pd.DataFrame(accuracies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def set_seed(seed: int = 42) -> None:\n",
    "    \"\"\"\n",
    "    Set seed for all underlying (pseudo) random number sources.\n",
    "    \n",
    "    :param seed: seed to be used\n",
    "    :return: None\n",
    "    \"\"\"\n",
    "    torch.manual_seed(42)\n",
    "    torch.backends.cudnn.deterministic = True\n",
    "    torch.backends.cudnn.benchmark = False\n",
    "\n",
    "\n",
    "def plot_performance(loss: pd.DataFrame,\n",
    "                     error: pd.DataFrame,\n",
    "                     accuracy: pd.DataFrame,\n",
    "                     log_file: str\n",
    "                    ) -> None:\n",
    "    \"\"\"\n",
    "    Plot and save loss and accuracy.\n",
    "    \n",
    "    :param loss: loss to be plotted\n",
    "    :param error: error to be plotted\n",
    "    :param accuracy: accuracy to be plotted\n",
    "    :param log_file: target file for storing the resulting plot\n",
    "    :return: None\n",
    "    \"\"\"\n",
    "    fig, ax = plt.subplots(1, 3, figsize=(20, 7))\n",
    "    \n",
    "    loss_plot = sns.lineplot(data=loss, ax=ax[0])\n",
    "    loss_plot.set(xlabel=r'Epoch', ylabel=r'Loss')\n",
    "    \n",
    "    error_plot = sns.lineplot(data=error, ax=ax[1])\n",
    "    error_plot.set(xlabel=r'Epoch', ylabel=r'Error')\n",
    "    \n",
    "    accuracy_plot = sns.lineplot(data=accuracy, ax=ax[2])\n",
    "    accuracy_plot.set(xlabel=r'Epoch', ylabel=r'Accuracy')\n",
    "    \n",
    "    fig.tight_layout()\n",
    "    fig.savefig(log_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Attention-based Network</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "set_seed()\n",
    "network = Attention().to(device=device)\n",
    "optimiser = AdamW(params=network.parameters(), lr=5e-4, weight_decay=1e-4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 style=\"color:rgb(0,120,170)\">Operate Attention-based Network</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "losses, errors, accuracies = operate(\n",
    "    network=network,\n",
    "    optimiser=optimiser,\n",
    "    data_loader_train=data_loader_train,\n",
    "    data_loader_eval=data_loader_eval,\n",
    "    num_epochs=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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uxQ7071GIaES7eYejUYl9NR7sqmpCMNTc7pE5hi8Y3S02QrcoL6fD0+F0Nrk2M4b1LsKw3s3/PpdSor4piJ0HGg9fkyZ8vvEgPvx6PwDAYjKgR4kD/XoUtrnGSmdktZrh94dSft6B3Qswot/x/8xogQXl47D2GAr3J28gemArjD1G6B2HiIiIjkGqUYTXvwelS08YywfqHYeIiDLcsmXLYj/PmTMn9vPAgQPxxhtv6BGJKCN8sakKf//3JuTbzfjp9SPQtfjYj9wfT77DghH9LLHCmpQSNQ2B5ukmDheZP169H5GodtPCCCFQWmjDGUPL0LOseaG60iIblAwvHreHEAKFeTkozMvByQOapwJSpURVna95FPOBJuw60IgPv9obmyO7sxNCQMrU9yUYjrKgrDdL1wEQuS7IkE/vKEREpLO///0ZAMBNN/1Q5yTUJqEg+OWbMA05lwVlIqJOhvfY7MNrTt+lSolFn+zEok93oX/XfNw6dZim00AIIVDstKLYacVpg0sAdP45bjs7RQiUFdlRVmTH2KFlADLrmmRSX9rCgvJxKEYzHNMf0TsGERERnYAQAsJRAOmp0zsKEREREXVAMBzF39/aiJVbanDGsDLMuHBAUuc4JqLEsaDcDjLkBxQDhLHzTpJORJTtXnrpBfznP+8hGlVx2mljEIlE4HKVYPr06wEA99wzCxMmTEK3bt3w+OOPwO/3o76+Dt/73g247LIrdU5P7aHYCyG99XrHICLKOrzHZh9ec9JKXWMAT85bhz1VrRffI6L0xYLyCUQP7YZv/gPIOf9WmHqN1jsOEVGn5Vv8cJvbbVN+AQAILH8Zau2eFvuEEDCPmQ5Dlx4Ib/kY4a2fHPP9x/PZZ8uxZcsmzJnzIoQQ+PWvf4UePXri/fffxfTp18Pn82L9+nW4//7f4umn/4zvf/8mjB59Kvbv34cbbriWH3w6CWFzInpwq94xiIhSjvfY7NLe6/3d+Tstp1+b8PUGeM1JOzsqG/Hkm2sRDEUx88rhOOkEi+8RUfpgQfkElIIKwGBEtHITC8pERJ3UypVfYOPG9bjppu8BAILBAEpKShEKBbFv316sW7cG48adCZPJhNtvvwOff74CL730PLZv3wa/n/PodxaKoxARrxtSqhCCj0kSEaUC77HZh9ectPDZxoN4/u3NyLeb8bPvjUCFK77F94hIHywon4AwGGEo7Y9o5Wa9oxARdWonGvWSM/a6VtuMRgWRSPPKy6YBZ8I04My4zq2qUVx99XRMm9b8GGZTUxMMBgPsdgc++GAp1q9fi+uvvwEA8KtfzUZubh7GjTsT5503Ae+//25c56TUM3QdCrPRAqhRgPPuEVEW4T02u7T3eh99jY+WyPUGeM0pMaqUWPDxTry1vHnxvdumDkOuhovvEVFq8NNWOxjKB0Gt3w/V16B3FCIiisOoUafg3Xffhs/nQyQSwS9+8VN8+OEHmDDhQixb9h727duL4cNHAAC+/PIL3Hzzj3DmmWfjs8+WAwCi0aiO6am9jOWDYBl1CYTBpHcUIqKswXts9uE1z0yRqApfIJLUcwRDUfx1wXq8tXwXzhxehp9NH8liMlEnxRHK7WAsH4QQgOiBzVD6nKZ3HCIi6qAzzhiPbdu24gc/uAGqGsVpp43FRRdNhhAC+flODBkyLLb4x4033oIf//hmWCxm9OnTD2Vl5ThwoFLnHlB7yEgI0aptUPJLoDiK9I5DRJQVeI/NPrzmmae63offv/I16puCKCm0oVdZLnqV5qFXeR66FztgNhkSPkddYwB/nrcWe6s9mHZuX1xwChffI+rMhDx6lv4MUVvrgaom3i2XKxc1NU2QahS+f90D00kXwTzwLA0Spt6RvnR2mdIPIHP6kin9ADKnL+nQj4MHd6O0tEfC7RzrUc109d1+K4pAUVHmzQen9X1WK6q/Ed6XZsIy9nqYh56vWbsnkg5/5rSSKX3JlH4AmdOXTOkHkB590eI+29nvsUBm3mfbusfGe7072zU+2tF9Toc/c1pJx77UNgTwu5dXIRiO4ryTu2JPVRN2HmiE2xMCABgUgYoudvQqz0Ovsjz0LM3FiEGlqKvztvsc2ysb8NS8dQiGo/jRpUMwvE96LL6XjtcjXpnSl0zpB9D5+3KieyxHKLeDUAywX/M7vWMQERHRcYgcB6AYIb11ekchIiIiSntuTxCPvPY1fMEI7po+Ej1Kc2P76puC2HWgETsONGLXgUZ8uaka/13dPLrcbDKge4mjeRRzWS56leeh2Gltc8TxZxsO4h9vb4bTYcbPpnHxPaJMwYJyB8iQH1AUCKNF7yhERET0HUIoEPYCqN56vaMQERERpbVGXwiPvrYaDd4QfnbNiBbFZAAoyLWgINeFkf1dAAApJardfuysbMTBhgA27qjFf1fvx3srm0fB23OM6Fmai55l345k/nD1fry1fDf6d3PitsuHcr5kogzCgnI7qe6D8P7rbuScdRNM/cfpHYeIiIjaoNgLOEKZiIiI6Di8gTAee201Drn9uPPqk9CnIv+E7xFCoKTAhpICW+xR/qiqYn+NF7sONmFHZfNI5nc+2wP1qJlVx59UhusnDIDRoCSzS0SUYiwot5PILwbMVkQqN7OgTETUDlLKrFpoIwOXJOiUhL0Q0ZqdescgIkq6bLrP8h7L603a8Qcj+OPra3Cg1ouZVw7HgO4FcbdlUBR0L8lF95JcjD+pHAAQCkexp9qDnZWNyHeYccrA4qz5vUuUTVhQbichFBjLBiJauVHvKEREaU9RDIhGIzAaTXpHSZlwOASDgbdVvRlK+wL80EJEGS7b7rPRaASKYtA7hm54vUkrwVAUf/rXGuypasJtlw/D0F5Fmp/DbDKgb0U++rZj1DMRdV585qADDOWDID21UBtr9I5CRJTWrFYHmprckLJzrizeEVJKhEJBuN01cDicesfJeuYh58N67g/1jkFElFTZdZ9V0dRUD6s1exfy4vUmLYQjUTz55lp8s78Bt0wZjBH9uugdiYg6MQ6l6gBD+SAAQLRyE5Q8l85piIjSl8ORj/r6GlRV7QMQ/2OLiqJAVdP/w5PBYERubgGsVrveUbKelBII+QCDCcLIhV+IKDNpcZ/tLPdYQMBszoHDkb2jHeO93p3nGh+N1zsZIlEVf5m/Hht31eOmiwfh1EElekciok6OBeUOUArKoRR1g4yG9I5CRJTWhBAoLCxOuJ0jC34QtZdaswO+Bb+GdeIdMPYYoXccIqKk0OI+y3ts5xHv9eY1JgCIqiqeXbQBa7fXYsbEARg3rEzvSESUAVhQ7gAhBOxX/FrvGERERHQMwl4IAFC9dTonISIiItKXKiX+8e9NWLmlBtPO64ezR1boHYmIMgTnUI6DDPkhQ369YxAREdF3CGs+IBRIb73eUYiIiIh0I6XEi0u2YMWGKkwd3xsTTummdyQiyiAsKHeQ6qmD5/9uR3jbCr2jEBER0XcIRYGwOTlCmYiIiLKWlBKvvv8NPlpTiclje2Dy2J56RyKiDMOCcgcJewGELR/Ryk16RyEiIqI2CHsBRygTERFRVpJSYt5/d+D9r/ZhwindcPmZvfWOREQZiAXlDhJCwFA+ENHKzc0ryRMREVFaUfKKAQi9YxARERGl3FvLd+Htz3bj7JEVuObcvhCC/yYiIu1xUb44GMsHIfLNcqj1+2Eo7Kp3HCIiIjqK9dwf6h2BiIiIKOWWfL4H8z/eibFDS3H9hP4sJhNR0nCEchwM5YMAgNNeEBEREREREZHulq3ah7n/2YZTBhbjfyYNhMJiMhElEQvKcVByu0Ap6QuoUb2jEBER0XdE9m+E55WfIlq/X+8oREREREn38dpK/HPpVozo2wW3TBkMg8JSDxElF6e8iJP90nv1jkBERERtMZggPbWQnjqgoELvNERERERJ89nGg3jh7c0Y0qsQP75sCIwGFpOJKPn4N00CZDgAGfLpHYOIiIiOotgLAACqt07nJERERETJ89WWGjy3eBP6dXPi9qnDYDIa9I5ERFmCI5TjJAMeeF76f7CceiXMJ12kdxwiIiI6TNicAASkt17vKEREREQAALcniIAK1NV5NWlvX40HcxZvRK+yXPy/K4fDYmIxOdPIkA8wWiAUXltKPywox0nkOKDkuRCp3MSCMhERURoRBiOENQ+SI5SJiIgoDXyxqQrPLNoAKbVtt3uJA3defRKsFpZ2Mk20dg98i34LxVkG64SZsSfwiNIF/9ZJgKFiMMLfLIdUIxAKfymJiIjShbAXQOUIZSIiItLZnqom/OPtTehdnocrzumPxia/Ju0qQmBIr0IWkzOUUlAOU98xCG/7DL75D8A68f/B4OqldyyiGP7NkwBD+UCENy6DWrMLhpK+eschIiKiw6wTZkKYrXrHICIioizm8Yfx1JvrYLMYcfvlw9C3VxfU1DTpHYvSlIxGEFzxCkwDz4KhSw/knHkDTEPOg3/JE/At+i1yzr4Zpj6n6R2TCAAX5UuIoWwgACBSuVnnJERERHQ0xVHIgjIRERHpJqqqeGbherg9Qdw2dRjyHRa9I1EaU/2N8P/7DwhvXIboUTUmQ2E32C6/D4YuPRH6+i1INaJjSqJvJXWEssfjwbRp0/C3v/0NXbt2jW3ftGkTZs+eHXtdV1eH/Px8vPXWW5g/fz4ee+wxFBUVAQDOPvts3HnnncmMGTfFmgdDxWAAGk+ERERERAmJ7FuP8IYPkHPuDyFMOXrHISIioiwz7787sGFXPW64aCD6lOfrHYfSWLRuH/zvPgHpa0DOuT+Cqe+YFvsVax6sk++CDHohFCNUbz2ExQZh5JcUpJ+kFZTXrFmDe++9F7t27Wq1b9CgQVi4cCEAwO/346qrrsL9998PAFi/fj1mz56NyZMnJyuapmwX36V3BCIiIvoO6W9EZPfXkN56CGeZ3nGIiIgoi3y+sQpLPt+Dc0ZWYPxJ5XrHoTQW2bMa/g/+BmHKgW3KL2Ao7t3mccJggrA5IVUV/iV/BIQC64T/B8VRmOLERM2SNuXF3Llzcd9996G4uPi4xz3zzDM45ZRTMHr0aADAunXrMH/+fEyZMgU/+9nP0NDQkKyImpHhIGTQq3cMIiIiOkzYm/9xzYX5iIiIKJX2VDXh+bc3oV/XfEw/v5/ecSjNyXAIirOseVqLYxSTjyYUBZbRV0BtqIJvwYOIVu9IQUqi1pJWUH7ooYdiReJjaWpqwty5c3H77bfHtrlcLtx6661YtGgRysrK8OCDDyYroiZkOADPi7chtP59vaMQERHRYUdGa0hvnc5JiIiIKFscWYTPbjXh1suGwmjgslXUmoyGEd62AgBg6nMqbJf+Eoq9oN3vN/YYAdul9wIGI3yLH0Z422fJikp0TEmdQ/lEFi1ahPPPPz82XzIA/OUvf4n9fPPNN+OCCy7ocLtFRQ5N8gGAy5V7giNyEXJ1h1KztR3H6ivd87VXpvQDyJy+ZEo/gMzpS6b0A8isvlDqCJsTAKB6WFAmIiKi5IuqKv52eBG+n183iovwUZtUfyMC7z2F6MGtUPJKYCjuDaF0/IsHQ2FX2C77FQLvPYXAsr8h0K0bYKlIQmKitulaUH7//ffxwx/+MPa6qakJ8+bNww033AAAkFLCYDB0uN3aWg9UNfGF8lyuXNTUNJ3wOFncH4H176P6QC2E0ZzweZOhvX1Jd5nSDyBz+pIp/QAypy+Z0g+g8/dFUYSmX3JS+wmjGSInF9Ln1jsKERERZYF5H+7Axl31+B8uwkfHEK3bC/+SJyD9jcg578ftmuLieBRrHqwX34XI9s9gqRiApkMeSFWNq0BN1FG6/S6TUmLDhg0YOXJkbJvNZsNzzz2HNWvWAAD++c9/xjVCOdWM5QMBNYJo1Ta9oxAREdFhOWffDNPgc/WOQURERBnus40HseSLPThnVAXO5CJ81IbIrq/hW/gQoEZhu+RumPqcpkm7wmCEqf8ZEEIgvGsVfAse5BN6lBIpLSjfcsstWLduHQCgrq4OJpMJFsu3j4EYDAY88cQTuP/++3HRRRdhw4YNmDVrViojxsVQOgAQCqKVm/SOQkRERIcZu58EQ2FXvWMQERFRBttT1YQX3t7cvAjfeVyEj1qTagTBL//17eJ7rl5JOY9QjFAbDsI3/wFEq7cn5RxERyR9yotly5bFfp4zZ07s56KiInz66aetjh89ejTmz5+f7FiaEmYrDN2GAULoHYWIiIgOixz8BtGDW2EZcbHeUYiIiCgDtViE7/JhXISPWpDRMGTI3zw1xUU/hchxQBiTN7e2sftw2C79JfzvPgHf4oeRc9ZNMPU9PWnno+zGv+00YrvwTlhGT9U7BhERER0WrdyE0Bf/goyE9I5CREREGSaqqvjrgvVwe0K47fJhyLen53pKpA/V3wj/W3+Af8njkKoKxVGU1GLyEYbCCtgu/xUMxX0QWPYMQmveTvo5KTuxoKwhGQ5CBjx6xyAiIiIAir0AALgwHxEREWnujQ+3Y9PuenxvYn/0Ls/TOw6lkWjt3uZpJw7thvmki1K+SJ6SkwvrpFkwDT4XhrIBKT03ZQ8WlDUioxF4/vn/EFz9b72jEBEREQBhLwQALkxCREREmvpsw0G8+8VenDuqAmcO5yJ89K3wrlXwLfzNt4vv9T5VlxzCYETOGTNgKO4DqaoIrHgVqqdWlyyUmZI+h3K2EAYjDEXdET2wWe8oREREBEA4Do9Q9rKgTERERO0nIyFEdq8GouHYNmPf0yAUIyrXr8TaD9ZgcmkOJnU3I7z1EADAUD4IiqMQ0br9UA/tatWmUlABuIZBBjyI7FnTar/IscPYfQQAILztM0CNtjrmSIbI/o2Q/kYYe4yEMCV/GoV4qN56yKA3qxZIjlZtQ2Dpk1BcPWGdMDP2tJzeVPcBhDd/hMi2FTCPngphMAEAlC49YCjsCtVbj+j+ja3eJ2xOGLsOgVRVRLataN2wEDD1GwsAiOxZCxloanWIodswKNY8RA/tglq3v9X+72ZoOpiDcGNA1wxa/Toc3ZdUZkgVFpQ1ZCgfhNDXiyCDXgiLXe84REREWU2xNf8jXvXW65yEiIiIOpPIji8R+HBOi22OXiejKaDiwCeLMN26BwgB4Y+AIyVn68Q7mgvKe9ci+Pnrrdo0Db8QGDwMalNNq7YBQOnSM1ZQDnz0PBAJtjrG0etkQDEitO5dRPesgbHnKFgnzEy4v1qTagTeV/4XSmFX2K/4td5xkk5KCSEElOI+sJzxPZj6nwFhTJ85tQ2FFbBddi/8S55A8OMXYtvNp1zZXMSs29fm70lD16Ewdh0CyGib+6EYY0XM4KqFUKu3tzrEdum9gDUPkR1fItTGE/3fzRD4zn49Mmj163B0X1KZIVVYUNaQoXwQsGohoge2wthzpN5xiIiIspowW2EeOQWG4t56RyEiIqJORPU0jzq2X/0woBgAAFHFiL8tXIeDnjGYedn30a04t8V7hDUfAGAaOB7GXie3btRsBdA8Utk+7Q+t9yvflmfsVx6jCHu4SJkz/n8Q3rgMoVWLENmzFsbuwzvUv2QLr/8AkBLmky/TO0rSqb4GBN7/C8wjJ8PYbTjMg8/VO1KbDAUVsF/9W8ijBlocGQhpKOvf9u/JwyOZoRjb3n8U6/m3AWqk1XZhcwIAzMMvgmngWa33fydDYaEddXVeXTO0EmeGo/uSygypwoKyhgwlfQCDCZHKTSwoExERpQHLKVfoHYGIiIg6GcVeCGOPkVCcZbFtcz/4Bpt21+PGSSPRo2/ZMd8rLPbjPrEsjGaIvOLjn/9E+21OmEdegsj2LxBY/k/Yy3+TNiNiVW89gl/Nh6HbcBi7j4DadAhKbhe9YyVFtHYP/EuegAx4ICPhE79BZ8JgavP3njBajvt7Ughx4t+zjsLjnzvHAZHjOPb+wxlMBblQIq2nrUhlhmPu72CGtvqSigypwkX5NCQMpuY5jAys0xMREaUD1X0QkUqub0BERETtZxpwJqwT/1/s9WcbDmLpl3tx3qiuOGP4sYvJqSQMRljGXQ/ZWI3Qunf1jhMT/Px1IBpBztjr4F/6JPxLntA7UlKEd30F38KHAEjYLr0bprZGpRNlMBaUNWY9/1ZYTrta7xhEREQEILTm3wgs+5veMYiIiKgTkQEPpKoCAHYfbMLz72xG/25OXHNeX52TtWTsOhSWsdfB1P8MvaMAAKKHdiOy7TOYR0yCkl8CY/lAqPX7oDbV6B1NU6H17zUvvldQDtvl98HQpafekYhSjgXlJJCRIFR/o94xiIiIsp6wF0L6GiDbmE+NiIiIqC3eeb9E4KPn0eQL4ak31yHXZsKtlw2F0ZB+JRTz0Aug2AsgIyG9o0Ap6o6cCT+BecTFAABjjxEAgMju1fqFSgKlqDuM/cbBNuUXUA7PjUuUbdLvb8NOTkoVnpf/F6GvFugdhYiIKOsJewEACelr0DsKERERdQJSqs3/brDm4W8LN6DBG8Jtlw9Dnj095ihuS7S+Et7XZyOyZ41uGVSfG0IImHqeDGG0AACU/FIo+aUZUVBWfW4Ev5wHKVUYywbAes4taTNvNZEeWFDWmBAKDMV9EOV8jURERLpT7M0LY0hPnc5JiIiIqDOQ/iZAqli9P4JNu+vx/QsHoFdZnt6xjkvJK4YwmhFY/rIuI5VVbz28r89GaO07rfYZeoxA9MBmyJA/5bm0Ej20G775DyK07l2odfv1jkOUFlhQTgJj+UCo7kqoPrfeUYiIiLKacBQAaP6gQ0RERHQi8vDn+M93BnD+yV0xblh6LMJ3PC0W6GujqJtswc9fB9QIjD1bL0xn7DkKhvJBkIGmlOfSQnjnSvgWPQQAsF1yDwxF3XRORJQeWFBOAkP5IADgKGUiIiKdKfZCGCqGQJitekchIiKiTmDjpp0AgC5lZbj63PRahO94jF2Hwtj7FIS+fgtqY+oWwYtUbmpeiO+kSVDyilvnKu0P26SftbkvnUkpEVy1CIH3noJS2BW2y38FQ5ceesciShssKCeBUtQDMFsRrdyodxQiIqKsJix22C6eBWO3YXpHISIiojS3cnM1/vPlDgRhwVUXjUzLRfiOxzJmOiAUBFe8kpLzSTWC4KcvQeR2iS3E1/ZxKqIHv4FU1ZTk0oQaRXTfehj7joFt8mwuvkf0HUa9A2QioSgw9hwNHJ6InoiIiPQj1QgQCkDkOPSOQkRERGlq9bZDeGbRBvQqPwmOq2fAYu585RLFUYiccdcfXpQ4+cIbP4RaXwnrhP8XW4ivLZGdKxH44OnmKSNK+6UkW7xUnxsy6IOhoBzWi/4XMFoghNA7FlHa6Xx/Q3YS1rNv0jsCERERAfC/80fISAj2S+/VOwoRERGloQ076/D0/HXoVuzAHVeehJxOWEw+wjTgTADNUzZASggleaOsTf3HAQYjDD1GHPc4Y9chgFAQ2bM6rQvK0UO74X/3TxBmK2xX/hrClKN3JKK01bme3+hkZCTIhfmIiCilFi9ejEmTJmHChAl4+eWXj3nchx9+iHPPPTeFyfQj7AWQnjq9YxAREVEa2rKnHk/OW4vSQjv+95oREJ+9AP/7T+sdKyEyGoZ/yeMIfb04eecI+SHMVpgHnX3CEbzCYoehtD8iu1cnLU+ijl58L+ecH0AIlsuIjod/QpJESgnv67MR/PxfekchIqIsUVVVhccffxyvvPIKFixYgNdffx3btm1rddyhQ4fw+9//XoeE+lDshZA+d+eat4+IiIiSbntlA554Yy2K8nPws2kj4LCaoNbvhwz79Y6WEGEwQZhyEFqdnAX6/LvWwfPK/yJavb3d7zH2GAG1fn9KFwxsj5aL73Xj4ntE7cSCcpIIIWAo7oNo5abmR02IiIiSbPny5RgzZgycTidsNhsmTpyIJUuWtDru3nvvxe23365DQn0IewEgVUh/g95RiIiIKE3sqWrC46+vQZ7NhJ9NG4k8uxkAIL31EFanvuE0YBkzLSkL9Ek1gkPvPgdhcUAp7Nbu9xkPT4sR2bNa0zyJih7citDKN2Hsezpsk3/OxfeI2okF5SQylA+C9NZBNqXXN3BERJSZqqur4XK5Yq+Li4tRVVXV4pgXX3wRgwcPxkknnZTqeLpR7IUAmj8gEhEREe0/5MWjr61GjsWAWdNGoiC3eUE5qaqQ/kYodqe+ATWgOAphOflSRHZ/relUE+H17yF8aB9yxl4LYTS3P09+KYz9x0E4CjXLogVj2QBYJ/+8eZqLDvSHKNt13pnmOwFDxSAAQGT/RpjzinVOQ0REmU5V1RZz2EkpW7zeunUrli5dihdeeAEHDx6M+zxFRY6Ech7N5crVrK1jCUa74oAtD3k2wJak86WiH6mSKX3JlH4AmdOXTOkHkDl9yZR+EHVEVZ0Pj776NQyKwKxpI9HFaY3tk4FGQKoQGTJK1TR0AsJbPkFg+cuwVwxOuGCqeusR/GohbH1PhqHHyA6/33r2LQmdX0uhjf9BnfRCDp4EY/kgveMQdTosKCeRkl8GYc1HtHIzMOhsveMQEVGGKy0txcqVK2Ova2pqUFz87ReaS5YsQU1NDa644gqEw2FUV1fj2muvxSuvdOxRyNpaD1Q18emcXK5c1NQ0JdzOCRm6wH79n+EF4E3C+VLWjxTIlL5kSj+AzOlLpvQDyJy+ZEI/FEVo+iUnZb5DDX488trXiKoSP79uFEoKbS32S58bADKmoCwMRljO+B6iB78BTrBwXnsEP3sdUCMomnAj3JGOv19KFWrNTsBohqED02VoTUoVoTVvQxQUwzRksm45iDozTnmRREIIGHuNhjDn6B2FiIiywNixY7FixQrU1dXB7/dj6dKlGD9+fGz/zJkz8e6772LhwoV49tlnUVxc3OFiMhEREVFnVN8UxCOvfo1AMIqfTRuBii72Vscohd1gn/4IjBWDdUiYHMbyQbCMugTCYEp4fSfzSRch58wbYCooja8BKeF7548IrWm9xkcqRfdvhGyqQd7IC3TNQdSZsaCcZDlnfA85Z96gdwwiIsoCJSUluPPOOzFjxgxcdtllmDx5MoYPH45bbrkF69at0zuernxLHkfg4xf0jkFEREQ6aPSG8OhrX6PJF8b/XjMC3Uvanu5FKAYouS4Is7XN/Z1ZaO07CHzwdFzvlWoUUlVh6NIDpv5nxJ1BKAYYuw1HdO9aSFWNu51EhTd9CGFxwDbwNN0yEHV2nPIiBWQkBBn0QrEX6B2FiIgy3JQpUzBlypQW2+bMmdPquK5du2LZsmWpiqW/cABqfaXeKYiIiCjFPP4wHn1tNWobAvjfa0agd3neMY8Nb1uB6P6NsIy/scU6FBlBApEdXyKy+2sYOzj/cXjduwhv/wK2yT9PuNhu7DECkW0rEK3eDmNpv4TaiofqcyOy62uYhp4PxWgGEEx5BqJMwBHKKeCd90sEl7+sdwwiIqKsJewFUL31escgIiKiFPIFIvjj66txsM6Hn1w5HP27OY97fPTAFkR2r868YjIA07ALoBSUI7D8FchIqN3vUz11CH61EMLm1GTktrHbMEAYEN39dcJtxSO6bwMgozBznSuihLCgnAKGkr6IHtgCKfV7pIOIiCibKfZCSG8978VERERZIhiK4ok31mBvtQe3Xj4UQ3oWnvA90teQMQvyfZdQjLCM+x5kUw1Cq//d7vcFP3sNkCpyxl6nTQ6zDYay/ojsWa1Jex1l6j8O9umPQnGW6XJ+okzBgnIKGMsHQQaaoNbt1zsKERFRVhL2AkCNQAY8ekchIiKiJAuFo/jzvLXYvr8BP7xkCEb07dKu96k+N4TdmdxwOjKWD4KxzxiE1vwbamP1CY+P7N+IyI4vYB5xMZQ8l2Y5TEPOg2nAmSmfR1mG/JBSQslt3+8HIjo2zqGcAobyQQCAaOVGGIq66ZyGiIgo+wh786gk6a0DrMeeO5GIiIg6t0hUxdML1mPz7nrcPHkwRg8sbvd7pbceSkHXJKbTn2XMNYAaAU4wrYeMRhD89CWIXBfMJ03SNIOp12hN22uvwH//DhnwwDZlti7nJ8okHKGcAoqjCCKvGNHKzXpHISIiykrGrkNgv/aPUAq76x2FiIiIkiSqqnhm4Qas3V6L7104AKcPLW33e6WqQvoboWTwCGUAUOwFsF5wO5TcE4w4VhSYhk1EzhkzIIxmzXNE6/YivOVjzds9liOL8Smunik7J1EmY0E5RYw9TwYsdr1jEBERZSVhyoHiKIRQ+E8fIiKiTKSqEn9/axO+2lqD6ef1w9kjKjrYgkTOBbfB2OfUpORLN5GDW+F7+9E2F+iTUoUQCsyDzm5eRC8Z5/9mBQIfvwAZ8iel/e8Kb/mkeTG+gWel5HxEmY6fqlIkZ8w1sJ59s94xiIiIslbgv/9AePNHescgIiIijalS4sV3N+OzjVW44qzeuOCUjk81KRQDTD1PhqEwS6apVKOI7lvf5gJ9gWXPIrDi1aSe3tD9JECNIrJvXVLPAzQXyMOb/wtD2UAuxkekERaUU0hGQpAhn94xiIiIdDf/ox244/EP8Z9V++APRlJyzsi+9Ygc2JKScxEREVHq/GfVfny05gAuGdcTF5/eM642ou5KhNa/lzUL+B5rgb7I/o2IbP8MwmJL6vkNJX0Bix2R3auTeh4AiO7fCNlUA9Ogs5N+LqJswYJyikg1Cs/zP0ZozTt6RyEiItLdkF7Ni+S9tHQrfvqXT/HPpVuw/5A3qecUjsLmRfmIiIjitHjxYkyaNAkTJkzAyy+/3Gr/f//7X0yZMgVTpkzBT3/6U3i9yb23UbMdlY0ozLPg0jN6xd1G9OA3CC5/GTIS1DBZerOMuQZQjAh8+k9IKb9diC+vGObhFyX13EIxwNhtOKJ71kKqalLPBTUCQ2l/GHudnNzzEGURFpRTRCgGCEch1MYavaMQERHprn83Jx6/4yzcM+NkjOznwkdrKvHL5z7HH15ZhZWbqxGJav/BQrEXQPXWa94uERFlh6qqKjz++ON45ZVXsGDBArz++uvYtm1bbH9jYyNmz56Nxx9/HIsXL8bAgQPx+OOP65g4e7g9QRTkWiCEiLsN6XUDAIQ1X6NU6U+xF8By8mWI7l2L6O7VCK1bCtV9ADljr03KQnzfZewxEjLoQbR6e3LP030EbJfcDWEwJfU8RNmEBeUUUvKKWzxKQkRElM2EEOhTno9bpgzGo7eNw5Vn90GNO4CnF6zHXX9djoWf7ITbo90oIWFvHqEspdSsTSIiyh7Lly/HmDFj4HQ6YbPZMHHiRCxZsiS2f9euXSgvL0ffvn0BAOeccw7ef/99veJmlQZvCE67JaE2pK8eIicXwmDUKFXnYBp6PgzdhkH1NyC0aiGMPUbC2H1ESs5t7DYU5lOvgpLbJWnniOzfCNV9MGntE2Wr7PqbUmdKnguRnV/pHYOIiCjt5NnMmDSmBy48tTvW7qjFslX7sPCTnXhr+S6M7O/CeaMq0L+bM6GRR4q9AIiEgJAPsNg1TE9ERNmguroaLpcr9rq4uBhr166Nve7ZsycOHjyIzZs3Y+DAgXjnnXdw6NAhPaJmHXdTEIO6FyTUhvQ1QNic2gTqRIRihO2in0INNEGt2QXzyItTd26zDZYRyTuflCoCH/0DSq4Ltsk/T9p5iLIRC8opJHKLIQNNkCE/hNmqdxwiIqK0oygCI/p2wYi+XVBd78N/vt6PT9YewMrN1ajoYsc5oypw+pBSWC0d/yeMscdIKPklAB93JCKiOKiq2uKLTSlli9d5eXn4/e9/j1/+8pdQVRVXX301TKaO3XOKihya5QUAlytX0/b0crx+BMNR+IIRVJTmJtTf/aFGmJxdkv5rlr7XJBfo9pN2H61VPyKeejR9/R4cw86GyVmsSZtH+LZ/DU/TIRSdPwOOY+RN3+vRcZnSl0zpB5BZffkuFpRTSMkvhrA5If0NLCgTERGdQHGBDdec2w+Xn9kbn2+qwrJV+/HPpVvxrw+3Y+zQUpw7sgIVrvZ/8FbyS5oLykRERHEoLS3FypUrY69rampQXPxtASwajaK0tBT/+te/AABr165Ft27dOnSO2loPVFWbqZlcrlzU1DRp0paeTtSParcfQHNxI6H+9jgF0mJP6q9ZtlyTjlAb6+D96HX4wgrMwyZq0uYR/s/egcjJha9wEPxt5M2U6wFkTl8ypR9A5++LoojjfsnJOZRTyNRrNBzXPwElv1TvKERERJ2G2WTAmcPLcd8Np+DeGaMxur8LH685gF/+/Qv8/uVV+GJTVbsW8ZOREELr3kW0atsJjyUiIvqusWPHYsWKFairq4Pf78fSpUsxfvz42H4hBG688UZUVVVBSokXXngBkyZN0jFxdnA3Na+34MxNbBE58/ALYRpwphaRqAOUvGIoznJE9qzRtF3V50Zk99cw9j+Di/ERJQELykRERNRp9C7Pw02TB+Ox28biqrP7oLYxgL8t3IBZf12O977ce/w3KwqCn72GyN51qQlLREQZpaSkBHfeeSdmzJiByy67DJMnT8bw4cNxyy23YN26dVAUBQ8++CBuvvlmXHjhhcjLy8NNN92kd+yM1+ANAQCcjvgX5ZORICIHt0IGvVrFog4w9hiBaOUWyJBPszbDWz4GpArzoLM0a5OIvpXUKS88Hg+mTZuGv/3tb+jatWuLfU899RTmzZuHvLw8AMDVV1+N6667DpWVlZg1axZqa2vRq1cvPProo7DbM2fhHN+i30Ip6o6ccdfrHYWIiKjTyrWZcdGYHph4anes21GLd7/Yg1c/+AYVLjsG9yxs8z1CMUJY8yG99SlOS0REmWLKlCmYMmVKi21z5syJ/Xz22Wfj7LPPTnGq7BYboZxAQVl1H4B/0W+RM+EnMPU8Wato1E6GHiOANW8jsnc9TH1O1aRNU58xEDm5fEKcKEmSNkJ5zZo1mD59Onbt2tXm/vXr1+OPf/wjFi5ciIULF+K6664DADzwwAO49tprsWTJEgwdOhRPP/10siLqQkYjUN2VescgIiLKCIoicFLfLrjjqpPgcubg5fe2Hnf6C2EvhOqtS2FCIiIiSia3JwijQcCeE/94Oel1AwAUm1ObUNQhhuK+EBYHIru/1qxNJc8F86CzNWuPiFpKWkF57ty5uO+++1osUnC09evX45lnnsGUKVPw4IMPIhgMIhwO48svv8TEic0TsU+dOhVLlixJVkRdKHnFUBur9Y5BRESUUcwmA649vz8O1Prw3spjT32h2As4QpmIiCiDuD0h5NstEELE3YbqcwMABAvKuhCKgpyzboJ55GRN2guseBWhTR9q0hYRtS1pBeWHHnoIo0ePbnOf1+vFoEGDMGvWLMyfPx+NjY14+umnUV9fD4fDAaOx+ZtFl8uFqqqqZEXUhZLngvTUQqoRvaMQERFllJP6dsGIvl2w6JNdqGsMtHmMcHCEMhERUSZxe4IJL8gnYwXlfA0SUTyMPUfCUFCRcDuqz43w+vegNmRWLYko3SR1DuVjsdvtLeaZuvHGG3H33Xfj2muvbfWtYjzfMhYVORLOeITLlatZWwDQVNEdNV9LFJgCMBWWadr2iWjdF71kSj+AzOlLpvQDyJy+ZEo/gMzqCyXf9PP74d7nPsfry7bhx5cNbbXf2P0kiBwHpKpCKFybmIiIqLNr8IZQVmRLqA3pdUPk5EIoupRI6LDQmncAkwXmwefG3QYX4yNKDV3+tqysrMTy5ctx5ZVXAgCklDAajSgsLERTUxOi0SgMBgNqamqOOWXG8dTWeqCqMuGcLlcuamqaEm7naBHRvAjhod27YIxqV/g+kWT0RQ+Z0g8gc/qSKf0AMqcvmdIPoPP3RVGEpl9y0om5nFZcPKYHFnyyE+N31WHIdxboM3YdCmPX1oVmIiIi6pzcTUEM6l6QUBvCUQhDxWCNElG8IvvWQ3rr4y4oS6kivPm/MJQP4mJ8REmmy9CcnJwcPPLII9i7dy+klHj55ZdxwQUXwGQyYfTo0Xj77bcBAAsWLMD48eP1iJg0huLesH/vzzBUDNE7ChERUUa6aEx3uJw5eKWNBfpkJIhI5ebYXIlERETUeYXCUfiCkYSnvLCMugTW836sUSqKl7HHCKjuyrjXnYru2wDZdAimQedonIyIviulBeVbbrkF69atQ2FhIR588EH8+Mc/xoUXXggpJf7nf/4HAHDfffdh7ty5mDRpElauXIk77rgjlRGTThhMUKx5CS0YQERERMdmMh61QN+XLRfok74G+N/6HaJ71+mUjoiIiLTi9oYAAE6HJaF2ZCSoRRxKkLH7CABAZPfXcb0/sm89RE4ujD1HaZiKiNqS9Ckvli1bFvv56HmTJ06ciIkTJ7Y6vqKiAi+99FKyY+kq8NnrEELActrVekchIiLKSCf17YKR/bpg0ae7cNrgEhTm5QD4dvV2LsxHRETU+bmbmgvB+Y74RyhLVYXn+R/DPOoSWE6+TKNkFA8lzwWloAKR3athHta6XnQiljHTYB5+IYSBc2ETJRtXo9GB6q5EhCOjiIiIkmr6ef2gSonXlm2LbRNGM0ROLqSnXsdkREREpAW3p7mgnMgIZRloBKQKkcOFoNOBsccIRA9shQz5OvQ+GfRCCAHFnth82kTUPiwo60DJK4baWA0pE184kIiIiNrWxWnFxaf3wMrN1diw69sRycJeyBHKREREGaDBk/iUF/LwugrC7tQgESXKNOhs2C7/FWCytvs9UqrwvnkfAstfTmIyIjoaC8o6UPKKgUgQMtCkdxQiIqKMdtFp3VHstOLlpd8u0CfsBZBejlAmIiLq7NyeIIwGAXtO/FMcSK8bAKAcnhaL9KXkumDo0qND605F962HbDoEQ0m/JCYjoqOxoKwDJc8FAJBxrlxKRERE7WMyGnDtBf1xsM6HpYcX6DOU9ofSpbvOyYiIiChRbk8QTocloUXv1SMjlFlQThuRPWvge+ePkGq0XceHN33IxfiIUowFZR2IvGIAgMqCMhERUdIN71N0eIG+nahrDMAyYhKsZ9+idywiIiJKkNsTSmhBPgBA2A8oBghbvjahKGEyEkJ071pEq7ad8FjVW4/I7tUwDTiTi/ERpRALyjpQcothnfQzGLoN0zsKERFRVph+Xj9ICbz2wTeQUkKGfJDRiN6xiIiIKAFHRignwjz8IjhumgOhsBiZLoxdhwKKAZHdq094bHjLx4BUYRp4VvKDEVEMC8o6EAYjjF2HQuEqskRERCnRxWnF5NN7YOWWGmxf9QU8L9yKaNU3esciIiKiBDR4QgkXlAFACJZG0okwW2EoG4jontUnPtZih7H/OCj5JckPRkQx/FtTJ+FtnyG05m29YxAREWWNC0/rjuICK+Z/1bwgHxfmIyIi6ryC4Sh8wQicCU554V/6JIKfz9UoFWnF2GMEVPcBqA1Vxz3OPOQ8TmVGpAMWlHUS2bcBoXVL9Y5BRESUNUxGA667oD+21zUv3KN663RORERERPFq8AQBIOERytHq7ZABjxaRSEPG7iMAAJF96455THj751A5QIBIFywo60TJc0H63JCRoN5RiIiIssaw3kUY2r8cPmmGv65G7zhEREQUJ7cnBAAJLconVRXS38AF+dKQkueC7YpfwzT43Db3q956BJY9g/D691KcjIgAFpR1o+QVAwDUxkM6JyEiIsou087riwbVjv179ukdhYiIiOLk1mCEsgw0AlJC2Au0ikUaMhR1O+b81uEtH3ExPiIdsaCskyMFZdlYrXMSIiKi7NIl3wqTsxgNniDW76zVOw4RERHF4cgI5YQKyl43AEDYnBokIq2pvgb4Fj+M8I4vWmyXqorw5o9gqBjCxfiIdMKCsk5iI5SbWFAmIiJKta5X/RyLTBfj5fe+QTii6h2HiIiIOqjBE4TRoMCeY4y7DelzAwAUFpTTksjJhVpficiuVS22R/etg/TUwjSIo5OJ9MKCsl4sdljGTIOhbKDeSYiIiLKOyajgugv6o6rOh6Vf7tE7DhEREXWQ2xOE02GGECLuNgzlg2C74tdQCis0TEZaEYoCQ/fhiOxdB6lGY9vDmz6EsObB2GOUjumIshsLyjoRQsA8/EIYuvTQOwoREVHWiexZjV6f/w7j+1iw+NNdqG0I6B2JiIiIOsDtCSU03QUACJOleZ5eY2LtUPIYu48Agl5Eq7bFtplHT4XlzBsgDPGPTieixLCgrKNo7R6Et63QOwYREVH2UYyQTTW4ZGTzqu6vffCNzoGIiIioI9yeIPId5oTaCG36EMGV8zVKRMlg7DoUUIyI7P46ts1Q1A2mnhydTKQnFpR1FNn2GQIfPgepcu5GIiKiVBK25tXcc+HB5LE98dXWGqzfwQX6iIiIOgstRihHdn+NyO5VJz6QdCPMVhjKB0Kt3gGpqvAteQKRPWv0jkWU9VhQ1pHIKwbUKKSvXu8oREREWUVxNBeUpbceE0/tjpICK15+bysX6CMiIuoEguEo/MEInAmOUJa+htiXzJS+cs75AaxTZiO6bx2ie1ZDRkJ6RyLKeiwo60jJdQEA1MZqnZMQERFlGZMVMOVA9dZ9u0BfvR/vfsEF+oiIiNJdgycIAAmPUJY+NxSbU4NElEyKNQ9CKAhtXHZ4Mb6RekciynosKOtIySsGwIIyERFRqgkhoNgLIL3NTwkN7V2Ekwe48NbyXTjU4Nc5HRERER2P29M8QjWRgrJUVUh/A4QtX6tYlES+JY8jumcNTAPO5GJ8RGmABWUdCUchIAyQjTV6RyEiIso61ov+Fzln3RR7Pe3cfoAAXvtg23HeRURERHpzHx6hnMiifDLQCEgJYeeUF52ByMkFAJgGnqVzEiICWFDWlVAMMA05F0pRN72jEBERZR0l1wVhtsZeF+XnYMrYnli1tQbruEAfERFR2tJihLIwWpBz9s0wlg/WKhYlUc4Z34Nt6gOxJ72JSF8sKOssZ+x1MPU5Te8YREREWSeyZzX8S5+EVKOxbRNO6Y6SQhsX6CMiIkpjDZ4gjAYF9pz4pz4QZitM/c+A4izVMBklizBaYOjSQ+8YRHQYC8o6kyEfotU79I5BRESUdVRPPSK7voL0NcS2NS/Q1w/V9X4s4QJ9REREacntCcLpMEMIEXcb0UO7Ed76CWQ0rGEyIqLswIKyzsKb/gvfggchg169oxAREWUVxdE8Z6L01rXYPrRX8wJ9/16+C74AP2QSERGlG7cnlNB0FwAQ2f01Ah8+ByRQlCYiylYsKOtMHJ7/R+XCfERERCkl7IUAANVb32rf2CGlCEVUVLv9qY5FREREJ+D2BBNakA8ApNcNYc2DUOKfNoOIKFuxoKwzJc8FAFAbq3VOQkRElF2UwwXl745QBoD8w6Oe3E2hlGYiIiKiE9NihLLqc0PY8jVKRESUXVhQ1pmSy4IyERGRLix2wGBqc4Sy8/CoJ7c3mOpUREREdBzBcBT+YCR2r46X9LkhbAUapSIiyi58tkNnwmyFsOZBNrGgTERElEpCCOSc9yMoea1Xd8+zmyEAuJtYUCYiIkonDZ7me3OiI5Slzw1DUTctIhERZR0WlNOAoXwwRE6e3jGIiIiyjqnnyW1uNxoU5NpMcHs45QUREVE6OXJvTrSgbOxzGgzFvbWIRESUdVhQTgPW836kdwQiIqKsFDmwBeqh3TAPm9Bqn9NhiY2CIiIiovTgjo1QTmzKi5zTp2sRh4goK3EO5TQhIyFIqeodg4iIKKtE965F8PPX27wH5zssHKFMRESUZo7cm/MTGKEsQz5Ea/dCRnifJyKKBwvKaSC8/Qt4/vEDyAbOo0xERJRKwl4AqFFIf1OrfU6HOTYKioiIiNKD2xOE0aDAnhP/A9fRA1vgm/dLqHX7NExGRJQ9WFBOA8LevLKsyoX5iIiIUkrYCwEA0lvfap/TYUGjL4SoyieIiIiI0kWDJwinwwwhRNxtqF43AEDYnNqEIiLKMiwopwElzwUAUBtZUCYiIkol5XBBWfXWtdrndJghJdDoDac6FhERER2D2xNKeEE+6XMDEBC2PE0yERFlGxaU04Cw5gNGM9TGGr2jEBERZZUjTwnJNgvKzR9WG7yc9oKIiChduA+PUE6E9LkhrLkQSvzTZhARZTMWlNOAEAJKbjEkRygTERGllLDmwjT8QihF3Vvtc+Y2F5TdTVywh4iIKF24PaGEFuQDANXn5nQXREQJ4NdxaULJc0GGA3rHICIiyipCKMgZM63Nffn25tFPXJiPiIgoPQRDUfiDkYRHKCs2J8CCMhFR3FhQThM5F/wEQuGAcSIiolRT3Qchgx4YSvq22J5nN0OABWUiIqJ04T48DVWicyjnjP8fLeIQEWUtVjDTBIvJRERE+gh+NR/+/8xptd1oUJBrN8Pt4ZQXRERE6aDh8D05kYKylBJSqlpFIiLKSqxipolozS54/3U3olXb9I5CRESUVYS9ANJbByllq31Ou5kjlImIiNLEkXtyIlNeSJ8bnuduQXjrJ1rFIiLKOiwopwlhyoFaXwm1oUrvKERERFlFsRcC0TAQ9Lba58y1xEZDERERkb6OPDWUyKJ80tcAyChgtmkVi4go67CgnCZEbhEAAbWxWu8oREREWUXYCwAAqreu1b58jlAmIiJKG25PEEaDAntO/MtBSV89gMML8xERUVySWlD2eDyYPHky9u3b12rf+++/j0svvRSXXHIJbr31VjQ0NAAA5s+fjzPOOAOXXnopLr30Ujz++OPJjJg2hMEE4ShkQZmIiCjFlMMFZdlGQdnpsKDRF0JU5VyLREREenN7gnA6zBBCxN2G6muuPQgWlImI4hb/13onsGbNGtx7773YtWtXq30ejwf3338/5s2bh5KSEvzpT3/Ck08+iXvvvRfr16/H7NmzMXny5GRFS1tKrgtqU43eMYiIiLKKyO0CQ8VgwNj68VlnrgVSAo3eMApyE1tRnoiIiBLT4AkltCAfAEhvPQABYcvTJhQRURZK2gjluXPn4r777kNxcXGrfeFwGPfddx9KSkoAAAMGDMCBAwcAAOvWrcP8+fMxZcoU/OxnP4uNXM4GSl4xJEcoExERpZRic8J28V0wlg9qtc9pb170h9NeEBER6e/ICOVEyKAHwpoLoSRtfB0RUcZLWkH5oYcewujRo9vcV1BQgAsuuAAAEAgE8Oyzz+L8888HALhcLtx6661YtGgRysrK8OCDDyYrYtoxj74ctit+rXcMIiKirCPVKOQxFuUDwIX5iIiI0kBzQTmxEco5474H+/RHNUpERJSddP1KrqmpCbfddhsGDhyIyy+/HADwl7/8Jbb/5ptvjhWeO6KoyKFZRpcrV7O2Tnyy5J4rpX1JokzpB5A5fcmUfgCZ05dM6QeQWX2h9OVb/DCE0QLbxbNabD/yoZUjlImIiPQVDEXhD0aRn+AIZQAQxsTbICLKZroVlKurq3HTTTdhzJgxuPvuuwE0F5jnzZuHG264AQAgpYTBYOhw27W1HqiqTDijy5WLmpqmhNtpL9XnRnDFazANOhvG8oGatp3qviRLpvQDyJy+ZEo/gMzpS6b0A+j8fVEUoemXnJQ8is0JtX5/q+15dhMEWFAmIiLSm9vbfC9OdISy7+1HYew+Auah52sRi4goKyVtyovjiUaj+NGPfoSLLroI99xzT2yFVpvNhueeew5r1qwBAPzzn/+Ma4SyFuoaA3jqX6uxbV/q5nAWihGR7Z9BPbQzZeckIiIiQNgLoHrrW203KApy7Wa4OeUFERGRro5MP5VIQVmqUUT3bYAMdN4BC0RE6SClI5RvueUWzJw5EwcPHsTGjRsRjUbx7rvvAgCGDh2Khx56CE888QTuv/9+BAIB9OzZE3/4wx9SGTEmElXx+fqDePez3RjWuwhTx/dGj9LkPnYtchyA2Qa1sSap5yEiosy1ePFi/PWvf0UkEsH3v/99XHfddS32v/fee/jzn/8MVVUxbNgwPPjggzCb+dinYi8EwgHIkB/CbG2xz+kwc4QyERGRzo7cixNZlE/6GwFICFu+RqmIiLJT0gvKy5Yti/08Z84cAMCwYcOwefPmNo8fPXo05s+fn+xYJ1RcYMOcu8/H60s3453PduOBF77EyQNcuOyMXqhwJe/xZSWvGGpjddLaJyKizFVVVYXHH38cb775JsxmM6ZNm4bTTjsNffv2BQD4fD48+OCDmD9/Prp06YI777wT8+fPxzXXXKNzcv0JewEAQPXWwWCuaLHP6bCwoExERKQzd9PhgnJuAiOUfW4AgGIr0CISEVHW0mXKi84ix2LEpDE98PsfjcUl43piw846/OrvX+DZxRtQVe9LyjmVPBdHKBMRUVyWL1+OMWPGwOl0wmazYeLEiViyZElsv81mw7Jly9ClSxf4/X7U1tYiLy9Px8TpQzgKAYsdMtj6/u50mGOP2RIREZE+3N4QjAYFNkv84+Kk1w0AHKFMRJQgFpTbwZZjxGVn9sYffjwWF57WHau21OCeZz/HC+9sQm1DQNNzKXnFkE2HINWopu22R3jbZwitW5ry8xIRkTaqq6vhcrlir4uLi1FVVdXiGJPJhP/+9784++yzUV9fjzPOOCPVMdOSoaQfcr//FxhL+7Xal2+3oNEbQlRV425fRkIIrv43ZMifSEwiIqKs5fYE4XSYY2swxUM9PEL5yJNJREQUn5TOodzZOawmXHVOX0w4pRveWrEb/129H8vXH8RZIyow+fQeyE9wtVkAMPYbC0PZQA3Sdkxk/0YElv0NMJhgHjYh5ecnIqLEqara4kOWlLLND11nnXUWPv/8c/zxj3/E/fffj8cee6xD5ykq0m7qJ5cruesTaKFbWR4kAFOOGUX51jaPOVE/6j58FeH9a1F0xsUwWNO7z53hmrRHpvQDyJy+ZEo/gMzpS6b0g7KDuymY0HQXAGDsPRpKYQWElU9oERElggXlOOQ7LLjugv648NTuWLx8J/6zaj8+XlOJ807uiovG9IDDaoq7bUNBBVBQceIDNRbZu7b5h2gYUlUhFA5eJyLqbEpLS7Fy5crY65qaGhQXF8deu91urF+/PjYqecqUKbjzzjs7fJ7aWg9UVSac1+XKRU1N+qyy7vv3I1CKuiFnzLQW2w1o7uv23XVQy1p/AD1RP1T3QXhXLICxz6mo8wDwpE+fvyvdrkm8MqUfQOb0JVP6AWROXzKhH4oiNP2Sk9JbgzeEii72hNpQcnKhlPKLFCKiRLFqmICi/BzccNEgPPSD0zBqgAtLPt+Du/66HAs+3gFfIBJXmzISQmjN24gc3Kpx2uOL7t/0bQZ/Q0rPTURE2hg7dixWrFiBuro6+P1+LF26FOPHj4/tl1Ji1qxZqKysBAAsWbIEo0aN0itu2pFBL9T6ylbbnYefQIpnYT4pJQLL/wkYTIBQEP5mecI5iYiIslHzlBeJjVAOrX8PoY3/0SgREVH2YkFZAyUFNvxgyhA8eNOpGNKzEIs+3YWf/2053v5sN4KhDs6FrBgR/HIeortXJyVrW2TAA7V2D5SSvs2vPbUpOzcREWmnpKQEd955J2bMmIHLLrsMkydPxvDhw3HLLbdg3bp1KCgowK9//Wv88Ic/xCWXXIKdO3di1qxZesdOG4q9ANJb12r7kQ+v8SzMF9n1FaL71sMy+nJED2xBZO+6hHMSERFlm2AoCn8winyHOaF2wls/QWT31xqlIiLKXpzyQkMVLgdumzoMuw42Yv5HO/HGh9ux9Mu9uPj0Hjh7RDlMRsMJ2xCKApHrgtpYnYLEzSKVmwBImPqfgWDVNqieOhhKUnZ6IiLS0JQpUzBlypQW2+bMmRP7+fzzz8f555+f6lidgrAXQm3jCaE8uwkCHR+hLCMhBFe8CqWwK0xDzkNkx5eQhxcDIiIiovZze5vvwYmOUJZeNwxdemgRiYgoq7GgnAQ9S/Nw59Un4Zt9bsz/aAdeff8bLPl8Dy4Z1xPjTyo/4aq0Sl4x1KaaFKUFopWbAKMFpt6nQAY9UApTP4czERGR3oSjAAh6IcNBCNO3H1gNioJcu7njU14YTLCcdg0URyGEYoCw5UOt26dxaiIiosznbjpcUE5gUT6pRiH9jRA2p0apiIiyFwvKSdSvqxOzpo/Ept31ePOjHfi/JVtgyzHhlIHFx32fkutCuOobSClPWHzWguW0a2AaMB7CYodlxOSkn4+IiCgdKfZCAID01kM4S1vsczrMcHdgygsZjUAYjDD1OTW2TdgLoO7boE1YIiKiLNLgbb4HO+3xT3kh/Y0AJAvKREQa4BzKSSaEwOCehbj7eycj327Gl5uqTvgeJa8YCPmBoDcFCQFhssDg6gkAiFZtQ4QfdomIKAsZe4yA/drHIPJaf/HrdFjaPUJZSgn/u08g8MmLLbYLmxMI+yHDHV/cj4iIKJtpMkL58LRTiq1Ai0hERFmNBeUUUYTAyP4urN1Ri1D4+Av1GcoHwnzqVUAKRieHd30F//tPQwY8AIDgqkUIfj436eclIiJKN8Jsg+IoglBa//PI6TC3e1G+IwvxKc6yFtuN3Ycj56ybUnJ/JyIiyiRuTwgmowKbJf6HrIW9AJax10Hp0l3DZERE2YkF5RQ6ub8LobCKDTtbryB/NEOXHrCMuBjCYk96puju1Yjs3wCYbQAAxVHY5gr3REREmU5KFf4P/47wts9a7XM6LGj0hhBV1eO3EQ4iuPwVKIXdYBp8bot9hsJuMA04E8KY2Ar1RERE2cbtDSLfbk5oSkjF5oR56AVQHEUaJiMiyk4sKKfQgO5O2CxGfLX1xAvuRfZvRPTQ7qRnilRugrFsYGw0lnAUQQaaICN8HJeIiLKLEAqiu79G9ODWVvvyHRZIAI3e8HHbCH29GNJbB8u46yEUQ4t9MhxEeOuniNbv1zI2ERFRxnM3BROa7gIAotU7ENn1tUaJiIiyGwvKKWQ0KBjRrwtWf3MIkejxRzgFlj2D8Ib3k5pHbayBbDoEQ/mg2LYj39ZKD0cpExFR9hGOAqht3AOdjuZRxcebR1l1H0Ro7Tsw9hsHY9mANg6IIPDhHET3rtcsLxERUTZwe0IJLcgHAOHNHyLw8fMaJSIiym4sKKfYyf1d8AUj2LLHfdzjlLxiqI3VSc0SqdwIADBUfFtQFocLyqqnNqnnJiIiSkfCXgjprW+13eloHhV1vIKysOXDNHQCLKdd3fYBZhtgMEL1tW6fiIiIjq3BG4zdi+Olet3NC+QSEVHCWFBOsSG9CmE2KSec9kLkFUNtPPHUGImIVm6GsOZBcZbHtil5xTD2PhXi8JzKRERE2USxFbS5lsC3BeW2F+aTUoUwW5Ez5hootvw2jxFCQNgKYqvMExFR57N48WJMmjQJEyZMwMsvv9xq/4YNG3DFFVfgkksuwQ9/+EM0NjbqkDKzBIIR+IPRhKe8kL4GFpSJiDTCgnKKmU0GDO9dhFVba6Cq8pjHKXkuSG89ZPT4czUmIufM78M6aVaLhQ0UewGs598KQ3HvpJ2XiIgoXQlHweG1BFoWjvPsJggADW2MUFZDAfjm/QrhHV+csH3F5oT0NWgVl4iIUqiqqgqPP/44XnnlFSxYsACvv/46tm3b1uKYhx56CDNnzsSiRYvQq1cv/P3vf9cpbeaoawoAAPITnPJC+uqh2J0aJCIiIhaUdTBqgAuN3hC2Vx77A6WSVwxAQm1K3ihlYcqBoahbq+0y4OGUF0RElJWMvU5BzoSZgGj5TySDoiDPbm5zygv3p29ArdsHYSs4YfvClt/mlBpERJT+li9fjjFjxsDpdMJms2HixIlYsmRJi2NUVYXX6wUA+P1+5OTk6BE1o9Q1NBeUExmhLNUopL+JI5SJiDTCgrIOTurTBUaDwFdbjl0sVgq7NU89kaRLFN76KfzvPw0Zaf3B2LfkcQQ+fC4p5yUiIkpnhoJymHqOgjAYW+3Ld5hbTXmhug/A/dliGPufAWNpvxO2b+x+Eoy9RmuWl4iIUqe6uhoulyv2uri4GFVVVS2OmT17Nu69916cccYZWL58OaZNm5bqmBmnrvFwQTmROZSjYRj7n8EncYmINNL60xIlndVixOCehVi1tQbXnNu3xZQTRxiKusF6/q1JyxDZtQrRQ7sAQ+vHhhRHEaKHdift3EREROlKhgMIb/kYhtL+MHTp0WKf02FpMUJZSonAp/+EYjIfeyG+7zANOFPTvERElDqqqrb47CalbPE6EAjgnnvuwQsvvIDhw4fj+eefx89//nM8++yz7T5HUZFD08wuV66m7enh043Ni9X37VEIhy3eaS9ygavu0CxTIjLhmgDsRzrKlL5kSj+AzOrLd7GgrJNR/V1Yu70We6o86FHa9m8wGfBARoJQHEWanltKFZEDm2HsMarNYrZwFELuXtXqH0hEREQZT0oEl78M86lXt1FQNmPXwabY68jOlYju34CiCTchZM1rX/ORINSGaih5LggTH4MmIupMSktLsXLlytjrmpoaFBcXx15v3boVFosFw4cPBwBcc801+NOf/tShc9TWeo671k5HuFy5qKlpOvGBaa6uMQCTUYHPE4Df2/oJ2/aQQS9kyAdhL4JQ9HtQO1OuCfuRfjKlL5nSD6Dz90VRxHG/5OSUFzoZ0a8LhAC+2nrsaS98b/0OgU9e1Pzcau1eIOiFsWJQm/sVRxEQjUAGOu9vfCIiongIsxUwWyG9da32OR0WNHlDiKoqAEDJL4FpwHjknTyx3e1Hq7bDN++XiNbs0ioyERGlyNixY7FixQrU1dXB7/dj6dKlGD9+fGx/jx49cPDgQezYsQMA8MEHH2DYsGF6xc0YdQ0B5NvNCQ12Cm//HN5XZ0H6uTAuEZEWOEJZJ3k2MwZ0c2LV1hpMHd/2PE5KXjFU90HNzx2t3AgAMJS3XVAWh0dES08t0M4RV0RERJlCsRe2uXCe02GBBNDoDaMg1wJDUXcYzroRQjG0u21hywcASJ9bo7RERJQqJSUluPPOOzFjxgyEw2FceeWVGD58OG655RbMnDkTw4YNw8MPP4w77rgDUkoUFRXht7/9rd6xO736pkBCC/IBgPQ1ABAQ/HxLRKQJFpR1NKq/C6+8/w0O1HpRVmRvtV/kuqDuXQcpVQih3WDySOVmKPmlUOxtr0av5Log8kshI6E29xMREWUyYS+A2sYI5XxH87yNTQf2wLzsdVjPugmKs6xDbSuHV5dnQZmIqHOaMmUKpkyZ0mLbnDlzYj+fddZZOOuss1IdK6PVNgRQVmRLqA3pq4ew5nXoS2AiIjo2Tnmho1H9m1cIXnWMaS+UvGIgGj78bap2rOf+CDkTfnLM/YaibnBc8zsYywZoel4iIqLO4HgjlAEJy5rXodbvByytvww+IbMNMJigsqBMRETULvVNATjt8S7G10z1uiEOf6lLRESJY0FZR4V5OehVlnf8gjIAtbFa0/MKsxWGgooTHielNotBEBERdSbGHiNhGnxOq+1OhwUjTLthq98GyylXQInjsVkhBITNyRHKRERE7RAIReALRDSZ8kLYndqEIiIiFpT1dvIAF3YeaEJtQ6DVPiW/BCK/FIiGNTtfaMP78H/wN8jDCwodi2/x7xD44K+anZeIiKizMPYcCcuoS1ttzzVHcbl9JZospTANOjfu9g3FfSBychOJSERElBUaPM3TMDodiY1QFjmODk9TRUREx8aCss5i015803qUspJX3Dz1RNehmp0vsvMrqO5KCOUEl14xQG06pNl5iYiIOgsZDiJyYAtUf2OL7dHVb8Gp+LAy/7wT30ePw3rej5Az9rpEYxIREWU8tycIAMh3JDZC2XbxLOSMmaZFJCIiAgvKuisttKHCZceqLW1PewHghKOJ20tGQohWfQND+aATHqs4iiA9tZqcl4iIqDNRm6rhX/wwogc2t9iu5JdipXISdoSLEz6HlNrc24mIKD719a3nyqf0446NUE6soExERNpiQTkNnNzfha373Gj0hlrt87//NHyLf6vJeaLV24FoBMZ2FJSFowjS3wCp4XQbREREnYFiLwQASE/LYoNp4HisyT83NloqXqEN78Pz3C2Q4dbTXRERUWpcfPHF+OlPf4qVK1fqHYWO48g9tyCBKS+iNTvR9MKtiOzfqFUsIqKsx4JyGhjV3wUpgdXbWk8xIcxWyIYqTc4T3b8REAoMZQNOeKziOPxhuo1V7omIiDKa2QYYzVC9dQCA8I4vEPjkJchIEE6HJTZaKl7CZAVklAvzERHpaNmyZRg7diz+8Ic/YMqUKXj55Zfh8Xj0jkXf0eAJwWxUYLUY425Det1AyAdhtmoXjIgoy7GgnAa6FTvgcubgqzamvRB5xZCBJsiQP+HzRA9sgeLq2a4bqXAUAQBUFpSJiCjLCCEg7IWQ3nrIkB/B5a8gWrUNUExwOsxo8oYQTWA6KmFzAgBUr1ubwERE1GE5OTm44oorMHfuXNx77734xz/+gTPPPBMPPPAAp8NII25PEIX5ORBCxN2G6mu+nkfuv0RElDgWlNOAEAIn9y/Gxl118AUiLfYpec3zNKqN1Qmfx3rhnbCe/YN2HWso7Q/Hjc/A2I7RzERERJlGsRdA9dYhuGoRpM+NnDO+B6EocDoskAAavfFPCSXsTgDgCGUiIp199NFH+MlPfoI777wT559/Pl577TWUlZXh1ltv1TsaHeb2BFGQm5NQG9LXAEBAWPO0CUVERIj/uRHS1KgBLiz5Yg/Wbj+EMUNKY9uVPBcAQG2qgaFLj4TOIczWdj/mIwxG8LcHERFlK0Npf0QrNyG8bilMA86EoaQvgG8XBWr+gBvfAkHK4RFSLCgTEennnHPOgdPpxLXXXotHHnkEOTnNRcsBAwbg9ddf1zkdHeH2hNCnmzOhNqSvHsKaB6EYtAlFREQcoZwuepfnId9hxldbW057cWSEskzwsdj6T9+E/8PnIKVs93v8H85BcNXChM5LRETUGZlPvgxQDIDJAvOpV8W25x9eFMjdlMDCfGYbYDBBBpoSTElERPF67LHH8M9//hNXXXUVFEVBbW1tbN8HH3ygYzI6mtsTRFFeYiOUVV8Dp7sgItIYC8ppQhECo/q7sG5HLYLhaGy7MNvguPEZmIeen1D73s0rIJtqOjT3lFpfiejBbxI6LxERUacUDUM4usBy6pVQjnpENjZC2Rv/wnxCCDi+/xQsRxWqiYgotQ4ePIjLL78cALB//35cfPHFWLZsmc6p6GiBUASBUBQFCRaUrRNmwjb5Lo1SERERwIJyWjm5vwuhsIoNO+tabBfG+B6pPUIGPAgd3AlD+eAOvU9xFEF66k58IBERUYYRRjOsZ98E8+BzW2zPs5sgRIIjlJH4vZ2IiBLzt7/9DS+++CIAoFevXpg/fz6efPJJnVPR0Ro8zV/eFiZYUBaKAcJi1yISEREdxoJyGunfzQl7jhFfbWk57UVozTvwvfNY3O1GDmwBIGGoGNSh9wl7IVRPbYemySAiIspkBkVBns0MtyexgnJw9dvwL2XhgohIL6qqorT027VrysrKoKqqjonou47caxOZ8kKqEfje+j3CO77UKhYREYEF5bRiNCgY0a8L1mw7hEj023/MyJAP0X0bINXocd59bNHKjRAmCwyu3h16n+IoAiJBIOiN67xERESZyOmwoCGBKS+A5gWCIvs3aJSIiIg6qrCwEK+99hoikQii0SjeeOMNdOnSRe9YdBT34RHKBXnxP9UjfY2IVm6CDHi0ikVERGBBOe2c3L8YvmAEm/fUx7YpuS5AqpCe2uO889iiB7Ygp9sgCIOxQ+8TjkIAgBrneYmIiDKR02FOfMoLWwEQDkCG/BqlIiKijnjwwQcxd+5cDB8+HMOHD8fcuXNx33336R2LjnJkhHJhvjXuNqTPDQBQ7E4NEhER0REdqzBS0g3pVQCLyYBVW2owtFcRAEDkFQMA1MZqKId/7gjbJfegwC7hDnfsfcbyQbBdcg8UZ+mJDyYiIsoS+Q4Ldh5sSqiNIx9spa8Bwhz/B2UiIopPz5498eabb6KhoQEGgwEOh0PvSPQdbk8QJqMCe44R/jgHGKuHC8rCVqBdMCIiYkE53ZiMBgzvU4RV3xzC9RMkFEXEishqY80J3t02YbbC5MwFajr24VfkOGAo7RfXOYmIKHGPPfYYfvrTn+odg77D6TCjyRtCJKrCaIjvYS9hcwJo/qDLL26JiFKvrq4OixYtgtfrhZQSqqpi9+7deOyx+NeuIW01eEJwOswQQsTdhowVlPM1SkVEREA7p7w4dOgQPvjgAwDAI488gu9///vYvHlzUoNls5MHuNDoDWHb/gYAgLA7AYMRamN1h9sKfvEGAp+8GHeW4KpFCG//Iu73ExFR/D788EO9I1AbnA4LJIDGBOZRPlJQPvJBl4iIUuuOO+7A8uXLMW/ePBw8eBALFiyAonBGyHTi9gThdMQ/fzJw+D4rBIQ1T5tQREQEoJ0F5dmzZ2Pv3r1YsWIFPv74Y1x66aX4zW9+c8L3eTweTJ48Gfv27Wu1b9OmTZg6dSomTpyIe+65B5FIBABQWVmJ6667DhdeeCF+/OMfw+vNvgXhhvUugtEgsGpr84hkIRTYLr0XlhEXd7it8PbPIb31Jz7wWO/f+ikiu76K+/1ERBS/rl274sYbb8RTTz2F559/PvYf6evIh9tEFuZTcrvAOuUXMHYdqlUsIiLqgMrKSjz77LMYP348rr/+erz66qvYsWOH3rHoKG5PCPkJFpRNA8bDetFPIRSDRqmIiAhoZ0HZ7XbjhhtuwEcffYTJkydj6tSp8PuPv4jMmjVrMH36dOzatavN/bNmzcKvfvUrvPvuu5BSYu7cuQCABx54ANdeey2WLFmCoUOH4umnn+5YjzKA1WLEkJ6F+GpLDaSUAABDl54QOR2b10ttqoFsqoGhYnDcWRRHIRflIyLSidPpRElJCfbv34+tW7fG/iN9OXPNAJDQwnzCaIaxbECH7+1ERKSNLl26AGieS3nr1q0oKSmJDXKi9NA8QtmcUBtKbhd+eUtElATtKiiHw2GEw2F8/PHHGDt2LPx+P3w+33Hfc2SV3OLi1ovI7d+/H4FAACNGjAAATJ06FUuWLEE4HMaXX36JiRMnttiejUYNcKG2MYA9Vc2rD0T2rIH/w7/HCsztEa1snpbEUD4o7hzCUQTpqYv7/UREFL+HH34YDz/8MG6//Xb86Ec/ir0mfeXbm0dLHVl9Pl6h9e8htPm/WkQiIqIOKioqwnPPPYehQ4di3rx5WLZsGQKBgN6x6LBAKIJAKIqCBEcoh9a+g/BOPnFLRKS1dhWUzzvvPJx++ukoKCjA0KFDcdVVV2Hy5MnHfc9DDz2E0aNHt7mvuroaLpcr9trlcqGqqgr19fVwOBwwGo0ttmejEX27QBECX21tnjdZbahCZOvHkIH2L6wX2b8RwpoHpaAi7hyKoxDSVw+p8tt6IqJU2717Ny6++GJcdtllmDp1Ks4//3xs375d71hZL89ughDNj+ImIrLjS0S+WaFRKiIi6ogHH3wQZrMZo0ePxtChQ/HnP/8ZP/vZz/SORYc1HL7H5ic4Qjm05h1E967VIhIRER3F2J6DZs6ciauvvholJSUAgEcffRQDBw6M+6SqqrZYqVVKCSFE7P9Hi2dF16Ii7R4fdblyNWurQ+cFMLRPEdZsr8UPrxgBb9fuqAKQb/Aix9W+AvGemm2w9RqG4uLmBQji6UtjaQUOSYmCnDBM+QUdfn8y6HVNkiFT+pIp/QAypy+Z0g8gs/rSUQ8++CBuvvlmXH755QCAefPm4YEHHsCLL8a/2ColzqAoyLObEx6hLGxORGv3aJSKiIg64ve//z3+8Ic/AGiejnHWrFk6J6KjHbnHJrIon1QjkP6m2EK4RESknXYVlA8dOoQNGzagtLQUjzzyCNavX49f/OIXcReVS0tLUVNT06L94uJiFBYWoqmpCdFoFAaDATU1NW1OmXEitbUeqGr7p4Y4FpcrFzU17R8RrLVhvQrx8ntbsWbTQZQYmgsqdbt3wmQua9f7cy5/ADLkR01NU9x9UR09YBn3PdQ3RiFC+v1aHKH3NdFSpvQlU/oBZE5fMqUfQOfvi6KIhL7krK2tjRWTAeCKK67ACy+8oEEySpTTbkloUT4AELZ8yL1ubQIREVGHbNq0qc0BTZQe6g8XlBNZlE/6GgFIFpSJiJKgXVNezJ49G3v37sWKFSvw8ccf49JLL8VvfvObuE9aUVEBi8WCr75qnsto4cKFGD9+PEwmE0aPHo23334bALBgwQKMHz8+7vN0dqP6N08LsmprDZTc5p/VpprjvaUFYbZBcRQllEFxlsI85DwuGkREpINoNAq32x17XVfHOe3ThdNhTmhRPgAQtgIgHIAMHX+hYyIi0l5xcTEuvvhi/OIXv8BvfvOb2H+UHo5MeVGQwJQX0ucGACh2pwaJiIjoaO0qKLvdbtxwww346KOPMHnyZEydOhV+f8c//Nxyyy1Yt24dgOZpMx5++GFceOGF8Pl8mDFjBgDgvvvuw9y5czFp0iSsXLkSd9xxR4fPkykKci3oXZ6Hr7bWQBjNEDYn1Mbqdr038MlLCHz2WsIZpJQI71yJaPWOhNsiIqKOuf7663HNNdfgiSeewJ/+9CdMnz4d06dP1zsWAXDmWhKe8uLIB1zpa9AgERERdcTIkSMxadIkVFRUwOl0xv6j9OD2BGE2KrBa2vVQdZvUwwVlYUuPqRuJiDJJu/52DofDCIfD+Pjjj/G73/0Ofr8fPp+vXSdYtmxZ7Oc5c+bEfh44cCDeeOONVsdXVFTgpZdealfb2eDk/i7868PtONTgR/6466HYT3wzlFJFZPvnMPQ4KeHzCyEQ+Oh5mPqcBkNx74TbIyKi9ps6dSp69OiBjz/+GKqq4r777sPYsWP1jkUA8u1mNPnCiETVuNswFPeBZex1gMWmYTIiImqP22+/Xe8IdBwNnhDyHeaEpiRRnKUwj54KJc+lYTIiIgLaWVA+77zzcPrpp2PQoEEYOnQoJk+ejMmTJyc7G6F52ot/fbgdq7YewoRTRrfrPWrdPsigB8bywZpkUBxFUD21mrRFRETtd+WVV2LhwoUYM2aM3lHoO5y5FkgAjd4Q2reyQWtKfgnM+RdoGYuIiNppypQpbW5fvHhxipNQW9yeYEIL8gGAwVkOw6hLNEpERERHa1dBeebMmbj66qtRWloKoHm6ingX5KOOKSm0oavLjlVbqnFePwOiu9fANOQ8COOx55KK7t8EADCUa3ONFEdRh+ZuJiIibVitVhw8eDB2/6X04bQ3f8h1e+JfmE9KFZHdX0NxdIGhSw+tohERUTv88pe/jP0cDofx73//G926ddMxER2t3hNC9+LE1vGJVm2DlBLG0n4apSIioiPaVVBWVRWLFy/GRx99hEgkgnHjxqFv374wGuOfz4jab1R/FxZ/uguefT4on78OQ/fhMBRUHPP4SOVGiPyShBfkO0I4CqEe2KxJW0RE1H5+vx/nnXceSktLYbN9Oy0CR0/pz5nb/MVuQ0LzKAsEPngGpsHnsKBMRJRip556aovXY8eOxbRp0/DjH/9Yp0R0tAZPEPm9CxNqI7hqEaS/AcapD2iUioiIjmhXRfixxx7D5s2b8f3vfx+qquL111/HH/7wB9x9993JzkcATh5QjEWf7sJWtxEDAcjGauAYBWUpVUSrtsHU+xTNzq84ioCQHzLkgzBznkciolSZNWsWzOb4Vzen5DnyGG4iC/MJISBs+bFV6ImISD/19fWorm7fAuiUXP5gBIFQFAUJTnkhffUQ9sSK0kRE1LZ2FZQ//vhjzJs3DyaTCQBw9tln45JLLmFBOUW6uuwodlrx5V6JgQDUxmNPPyGEAsf0RyAj8T+C+12KqzdMA8dDqlHEvyQCERF11COPPIKFCxfqHYPakGczQ4jmR3ITodgLWFAmItLBd+dQrqysxDXXXKNTGjpag7f53prvSOxLdelrgMHFheWJiJKhXQVlKWWsmAwAZrO5xWtKLiEERg1w4b0v9+B7rhyojcf/5lyYbZqOJDaWD4RRo/mYiYio/TiHcvpSFIE8uznBKS8AYXMiWrtbo1RERNReR8+hLIRAYWEh+vTpo2MiOuLIvTWRRfmkGoH0N0LYnBqlIiKioyntOWjgwIH47W9/iz179mDv3r14+OGH0b9//2Rno6Oc3N+FqAr4zQXHLSj7//Msgivf1PTcUkqojTVQOYKKiCiljsyhfN5552HKlCmx/yg9OB2WhBblA3B4yosGjRIREVF7de/eHW+//TZOPfVUFBUV4bHHHsOhQ4f0jkUA6rUoKPsaAYAFZSKiJGnXCOX77rsPv/nNbzBt2jRIKTFu3DhMnz492dnoKL3K8+B0mLEGg3FWr+5tHiOjYUR2fAnToLO1PbmU8L7+c5hPmgTLqVdq2zYRER3TPffco3cEOg6n3Yz6psRGKBtK+0GG/JBqBELhYsdERKkye/ZsnHvuuQCAiooKnHrqqfjFL36BOXPm6JyM3E3NX9Y6E5ryQsLYazSUwmMvZk9ERPFr1ycXh8OB3/3udy22jRo1CqtWrUpKKGpNEQKj+rswb20E4/uc0eYx0artQDQMY/lgTc8tFAXCXgDVU6tpu0RE1LbKykqUl5e3WoEeAD766CMdElFbnLkW7DzQmFAbpt6nwtS79XUmIqLkqq+vx4wZMwAAFosFN9xwAxYsWKBvKAIANHiDMBsVWC3xf9GqOIpgveB2DVMREdHR2jXlRVuklFrmoHY4ub8LSjSA7V99Dhnyt9ofrdwECAFDmfbTkSiOIkhvnebtEhFRa7fddlvs55/85Cct9j3++OOpjkPHkG83o9EXRiSqxt2GVKNQmw61eV8nIqLkiUajqKqqir0+dOgQP+OmCbcnBKfDAiHiXxJeBr3N//GaEhElRdxf+SXylzvFp393J/pb69Ft7WuIdp/daqG8aOUmKF16Qljsmp9bOIoQrfpG83aJiKi1oz/87N2795j7SF/O3Oa5Hd0JTHuhNlTB96+7kXPuD2Hqe7pW0YiI6ARuuOEGXHbZZTjzzDMhhMDy5ctx11136R2L0LwoX35C010AobVLEFr9Fhw3/R1g7YKISHOcrK8TMSgKynr0BKqASENVi4KyjIQQrdkJ89ALknJuxVGEyPYvIFUVQol7YDsREbXD0V/afvcLXH6hmz6OLBZU1xhAgTW+f1IpdicAQHLhWyKilLryyisxdOhQfPbZZzAYDLj55pvRr18/vWMRgHpPCN2LHQm1IX0NENZ8fnYlIkqS4376GTlyZJsfXKWUCAQCSQtFxzZgUG9EDwo07N2LroO+3S6MZji+9yfIaCQp51WcZVC6dAfCfiAJI6CJiOhbHIXcORxZLKi2IYACa5wffE1WwGiG6nVrF4yIiE6oqqoKr732Gu6//37s2LEDjz76KB544AG4XC69o2U9tyeIYb0LE2pD9bkhbE5tAhERUSvHLSi/9dZbqcpB7TSkdxfsX+aAr3p/q33CbEOyxq2Z+o+Dqf+4JLVORERHU1UVDQ0NkFIiGo3Gfgaa53yk9HBkhHJ9UwAoja+gLISAsDkhfQ1aRiMiohP4+c9/jnPPPRcAUFFRgVNPPRV333035syZo3Oy7OYPRhAMRVFw+B4bL+lzQ9gLNEpFRETfddyCckVFRapyUDuZjAaEcgohPDVQVQlFaS4h+9/9E5TiPrCMnJzU83PKCyKi5Nu6dSvGjBkTKyKfdtppsX2c8iJ95NnMEAKoa0jsqS3F5oT01WuUioiI2qO+vh4zZswAAFgsFtxwww1YsGCBvqEIDd4QgG+/tI2X9LlhcPXWIhIREbWBcyh3Qobywdi5eRsM+xvQv5sTMuhFZM9qmIu6J+2cMhyE5593wDLqEphPuihp5yEiImDz5s16R6B2UBSBPLsZdY0JFpQLKjiHMhFRikWjUVRVVaGkpAQAcOjQIU45lQaOLHSbyKJ8UlUBkxUit4tWsYiI6DtYUO6Eys+6Ar9f8wnO2VKD/t2ciBzYAkgJQ8XgpJ1TmJq/IVY9tUk7BxERUWfjdFgSLijnnPl9jdIQEVF73XDDDbjssstw5plnAgBWrFiBu+66S+dU5PY2F5QTGaEsFAWOab/XKhIREbWBcxd0QjlmA0b1sGLt1srm+TUrNwEGMwzFfZJ6XsVRBMmCMhERUUyBBgVlIiJKvSuvvBLPP/88Bg8ejGHDhuGaa67Biy++qHesrKE21sC/7BnIoLfFdneTNlNeEBFRcrGg3AmpdXtxnfuvKPXvwO6qJkT3b4KhrD+EIbkDzoWjEKqnLqnnICIi6kzyHWbUNwYTaiOy62s0/d9tUN0HNUpFRETtUVZWhlAohOeeew7/93//FxutTMknQ15Etn2G4FcLWmxv8AZhNiqwWgxxtx3ZsxqeV36KqLsywZRERHQsnPKiE1JyXQAAl6EJ6zfvxVnuSpj7nZ788zqKEKnekfTzEBERdRZOhwVuTxCRqAqjIc7v6U0WIOiF6quH4izVNiAREbWyY8cO/N///R8WLVqEiooKBAIBLFu2DLm5uXpHywpSqgit/wAiz4XwhvdhGnAmDIfXA3J7QnA6LAktQqx66iA9tRBmm1aRiYjoOzhCuRMSZitETi662wPYUhWGY8aTMA86O/nndRRBhv2QkVDSz0VERNQZHFk0qNEb/71R2JwAAOlr0CISEREdxw9+8ANcf/31MJlMePHFF/HWW2/BbrezmJxCMuBBZOvHMPUfB2FxIPjJS7EFEd1NwYQW5APQvNCtEBA5eRqkJSKitrCg3EmJPBfKzF7sqGyENNsgLPakn9M8fCIcN82BMCZ2gyciIsoUR+Z4dHviLygrdicAQPrqtYhERETHsXHjRgwZMgT9+vVDjx49ACCh0bDUcdLbPI2iUtgVllOvQrTqG0S+WQ4AcHtDCc+fLL1uCGs+hMJyBxFRsvBv2E5KyStGPhpxk/nfqP383yk5pzCYIAR/yxARER1RECsoJzCPsskKGMxQvW5tQhER0TF9+OGHuPzyy/HWW2/hjDPOwMyZMxEMJjYXPnWM9DR/garYC2EccAYM5YNii/O5PcGEC8qqzx17+oeIiJKD1cFOSnGWwxz1oZ+pCrVuf0rOKQMe+BY/jPCOL1JyPiIionR35LHcRArKQggIu5NTXhARpYDRaMSkSZPw0ksv4c0330RxcTGCwSAmTJiAV199Ve94WUE9PEJZ2AsghALrxbNgHjYB/mAEwVAUTg2mvBC2fC2iEhHRMbCg3ElZRl0Cy7jrAQAbg8WpOak5B9GDW6HW7U/N+YiIiNJcns0MRSQ25QUA2C/7FXLOuUWjVERE1B59+/bFvffei48++gg33XQT5s6dq3ekrCC99YAwQFib5zgWQoGMhOBbuRAVhrqERyjbpvwCOeNv1CIqEREdg1HvABQ/tXIz/MKKr2ssmJqC8wnFCGFzQvXUpuBsRERE6U9RBJy5OYlNeQFA5Dg0SkRERB1ltVpxzTXX4JprrtE7SlYw9h4NxVnacjrFaBiGb/6DK21WmO1nJ9S+MFshzNbEQhIR0XFxhHInJQMeRL75FFbpR+UhH3yBSErOKxxFkCwoExERxRTmWdCQ4Ajl8DfL4XvnjxolIiIiSl+GLj1h6n9Gi23CYsfBHheit6kGRXWr425b9bnhX/pnRA5+k2BKIiI6HhaUOyuLDcbep6Jq1A8hAew82JiS0yr2wticV0RERAQU5lkTHqEsfW5E966FDKVmXQQiIiK9hLd8jGj19lbbd1mHYkfYBeuGhbFF+jpKemoR2bUKiPP9RETUPiwod1JCKLCefyvKh54CAWDH/tQs5HNkhLKUMiXnIyIiSneF+RpMeXF4NXrpcyceiIiIKE1JKRH45CWEt7de6N3tDWNhcAwQ9CC48s242lcP30eF3ZlASiIiOhHOodzJ2XKMKOtix/bK1IxQNg85D6b+4wBIACIl5yQiIkpnhbkWNPnCiERVGA3xfVcv7AUAmj8IK84yLeMRERGlj6AXiIag2Atb7WrwhuCzlcM0+FyoTYcgVRVC6dh9VXrdAL79opaIiJKDI5QzQJ/yPGzf35CSUcNKbhcYCru2XECBiIjSxuLFizFp0iRMmDABL7/8cqv977//Pi699FJccskluPXWW9HQkJonXDJZYX4OAKDRG/88ysKWD4AjlImIKLOp3noAgHAUtNrnbgrC6TDDMmY6rBPv6HAxGTh8HxUCIicv0ahERHQcrApmgD4V+fAGIqiqT/68i2qgCYHlLyNatS3p5yIioo6pqqrC448/jldeeQULFizA66+/jm3bvv372uPx4P7778ezzz6LRYsWYcCAAXjyySd1TJwZCvKaC8r1CUx7odiaP1gfGVlFRESUieTh9XjaGqHs9gSR77BAGIwQQiCybwPCO7/qWPs+N4Q1P65iNBERtR//ls0Avcubv33dnoJ5lAUEwuvfa3MRBSIi0tfy5csxZswYOJ1O2Gw2TJw4EUuWLIntD4fDuO+++1BSUgIAGDBgAA4cOKBX3IxReLig3OCJf4QyTDmwXngnjH1O1SgVERFR+omNULa3MULZG4LTYQHQPNdy8Kv5CH78AqJ+T7vbNw2dgJyzbtQmLBERHRMLyhmgvIsdVosBO1Ixj7LFDhgtUD11yT8XERF1SHV1NVwuV+x1cXExqqqqYq8LCgpwwQUXAAACgQCeffZZnH/++SnPmWmKDheUE1mYTwgBY/eToDiKtIpFRESUdpT8EpgGjo9N9XSEPxhBMBSFM9cMoPm+mDPue5BBD+r/+2q72zcUdYOx23BNMxMRUWtclC8DKEKgV1leakYoCwHFUQjpqU36uYiIqGNUVYUQ3y6YKqVs8fqIpqYm3HbbbRg4cCAuv/zyDp+nqMiRUM6juVy5mrWll6gqoQggrCbWH8/GTxH1NSF/9IUapuu4TLgmQOb0A8icvmRKP4DM6Uum9IM6D2P5IBjLB7XafuRLWafdEttm6NIDpsHnonHVUth6jIGhS88Tth9a8zaUkr4wlvbXLDMREbXGgnKG6FOej7dW7EIwFIXFbEjquYSjCCoLykREaae0tBQrV66Mva6pqUFxcXGLY6qrq3HTTTdhzJgxuPvuu+M6T22tB6qa+EKwLlcuamqaEm5Hby5XLvLsZuyvbkqoP/41nyBauxuhHuM0TNcxmXRNMqEfQOb0JVP6AWROXzKhH4oiNP2Sk5IvWrcXwmyH4mg5h/KRaaOcDnOL7ZbRUxHd+SUCn7wE26X3HHdxeKlGEPx8LswnX8aCMhFRknHKiwzRpyIPUgI7DyR/2guOUCYiSk9jx47FihUrUFdXB7/fj6VLl2L8+PGx/dFoFD/60Y9w0UUX4Z577mlz9DLFx+mwJDTlBdA8nyQX5SMiokwW+OCvCC5/udX2I/fQfIelxXZhsaPovBmQnlrIpuN/BpW+5id2hc2pTVgiIjomjlDOEL3Lm+eg2l7ZgIE9Wi9woCVj/zNgKBuY1HMQEVHHlZSU4M4778SMGTMQDodx5ZVXYvjw4bjlllswc+ZMHDx4EBs3bkQ0GsW7774LABg6dCgeeughnZN3fk6HBbWNgYTaUGz5QCQIGfJDmK0aJSMiIkofqqcehvLBrba7YyOULa32OYadBX+XoRCmnOO2faSgrLCgTESUdCwoZwiH1YSSQltKFuYzlvYH+AgREVFamjJlCqZMmdJi25w5cwAAw4YNw+bNm/WIlfGcDjO2Vya2lsGREVXS52ZBmYiIMo4M+YGwH8Je2Gqf2xOE2aTAamk9faMQCoQpB6rPjei+DTD1b3tqKNVX33y83alpbiIiao1TXmSQPuXNC/NJmfi8lscjg16Et3wMtbEmqechIiLqLJwOC5p8YUSiatxtHCkoqz63NqGIiIjSiOptLvgqjtZP1Lo9wf/f3n3HR1Wl/wP/3DJ9kkwqKZRA6L0pkSoonYhYURQra9t11/3qrrp+F3XV3a/rrv5UbLi7uoIFlWpB7AWQ3ntvgSSQPpl+z++PQDSGEjLlzkw+79fLl8zce899DjfMyTz33OfAYTOdtRyXd+NiuL99HYHj+0+7/VTZKJa8ICIKPyaU40heThIqa3w4XhHcI7fnIjxOuL/9FwLHdoT1PERERLEi6eQiQpVOb5PbkJOzYex/BWR7aqjCIiIiihrCWQoAp52hXFHtbbAg3y+Z+hRAMifA/cNbEKLhDVwltTWMvcZBMieGJmAiIjojJpTjSF527cC550hwj9yei2SrvaOscWE+IiIiAD/VfCwLYmE+2eqAqe9lkBMzQhUWERFR9JBkyOltT3vjtLzaA0dCw/rJ9Q432WAacC204j3w7/ihwXYlswNMA66BJDPNQUQUbvykjSM56TaYDAr2hLmOsqQYIFmSIJhQJiIiAvBTQrmiuukzlAHAf3QHAiX7QxARERFRdFFzusI2aTrkhLQG28qrvUiynT2hDABqh4FQMjvCs/J9CHd1vW2Bot0IlBWGLF4iIjqzsCaUFy1ahHHjxmHUqFGYPXt2vW3btm3DxIkT6/4bMmQIJkyYAACYN28eBg8eXLft2WefDWeYcUORZbTNSgj7DGUAkOyp0KpLw34eIiKiWHDqMd3yIGYoA4D723/Du+GTUIREREQUVUTAf9r3XR4/PL4AHAlnL3kBAJIkwTToRgifC/7CbfW2ub9/E54Vc0ISKxERnZ0aroaLiorw7LPPYu7cuTAajZg8eTIGDBiA9u3bAwC6dOmCBQsWAABcLheuvvpqPProowCAzZs348EHH6xLMFPjtctOwmcrD8LrC8BoaLhCbqjI9hRoZUfC1j4REVEsSbAaIUtS0All2ZoE4Qr/jWEiIqJIcy15HvC5Yb3s4Xrvnxo7HY2YoQwASmor2K77B2RrUr33RU05lIy80ARLRERnFbYZysuWLUN+fj4cDgesVitGjx6NxYsXn3bfV199FRdccAH69+8PANi0aRPmzZuHgoIC3H///aio4BerxsrLSURAEzhQVBXW86htekPN7RfWcxAREcUKWZaQaDOgPMiSF5LVAe3kKvVERETxRDjLIJlsDd4/VS7qXIvy/ZxsTYLQNPh2/wghNIiAH8JdBcnmCFW4RER0FmFLKBcXFyM9Pb3udUZGBoqKihrsV1VVhTlz5uDXv/513Xvp6em4++67sXDhQmRlZeHxxx8PV5hxJy+79i7tniPhraNs6DgYpguvCus5iIiIYonDbgp6hrJkdUDUlEEIEaKoiIiIooPmLK1b4P3n6mYon2NRvl/yH1wH91evwLfj+7qneySrI+g4iYjo3MJW8kLTNEiSVPdaCFHv9SkLFy7EpZdeitTUn1Z6nTFjRt2fb7/9dowcOfK8zp2aam9CxKeXnp4QsrYiIT0dyEy14vAJZ4PYQ9kXze+F7/gRqI4MKOaGd5nDKdauydnES1/ipR9A/PQlXvoBxFdfKL457CYcr3AH1YZscwB+L+BzA0ZLaAIjIqKQWbRoEV5++WX4/X7cdNNNmDJlSt22bdu24cEHH6x7XVpaiqSkJHz00Ud6hBpVhN8DeJyQbCkNtp16uqcxi/L9nNqmL5TMjvCueB+ytTZRLTOhTEQUEWFLKGdmZmL16tV1r0tKSpCRkdFgvy+++AJ33HFH3euqqip8+OGHuPnmmwHUJqIV5fxqAZ84UQ1NC35mT3p6AkpKwls6IhxyWyRg694TKC6urEvih7ovgeK9qJn/OCyjfgs1t0/I2j2XWL0mpxMvfYmXfgDx05d46QcQ+32RZSmkNzkpujnsRuwpDK5Ml5zaGmpuP4iADxKYUCYiiibBrBPU3AlnGQBAPsMMZaNBhsV0ft/7Ty3QVzN3Ojxr5kHJ6QYpMf3cBxIRUdDCVvJi4MCBWL58OUpLS+FyubBkyRIMHTq03j5CCGzZsgV9+vyUkLRarXj99dexYcMGAMCsWbPOe4Zyc9cuOxHl1V6UVQX32O3ZSPbaGeVa9YmwnYOIiCiWOOwmVNX44A9oTW5DbdkdllG/gWxJDGFkREQUCsGsE9TcCVcVICmQ7KeboeyBw2467RPN56KktoKh2yXQSvbDdOFVUJJzQhEuERGdQ9gSyi1atMB9992HqVOn4vLLL8eECRPQs2dPTJs2DZs2bQJQ+wiQwWCAyfTToy2KouC5557Do48+irFjx2LLli144IEHwhVmXMrLOVlHuTB8dZQlSwKgqEwoxxjP6nlwfTEDWg0XuiQiCrVTtR8rgliYTwgB4XFCeF2hCouIiEIkmHWCmjslswPst8+Ektmpwbbyai8ctsYvyPdLpv6TICVmwH9oUzAhEhHReQhbyQsAKCgoQEFBQb33Zs6cWffn1NRULF26tMFx/fv3x7x588IZWlxrlWGHQZWx50gFLujcsMxIKEiSDMmWCsGEckwRrkr4965CoHA7zBffBrV1b71DIiKKG0knvwyXOz1ITTI3rRGfG9Vv3gPTgGth7DU2hNEREVGwglknqLFCXSorFtaiqHb5kNfScdZYz96PBNRMuAOewzuQHAP9jYVr0hjsR/SJl77ESz+A+OrLL4U1oUz6UBUZuZkJQddxPBfZngLNWRrWc1BomYfcBEO3S+H+6hW4Fj8HQ7dLYRpwDSS16TMCiIiolsNeO0O5vKrpM5RhMAOqCVpNeWiCIiKikGnqOkHnI1TrAQHRtRaFZ818BEr2wzrmdw22nahwo1uufMZYG9UPezugc7uo6e+ZRNM1CQb7EX3ipS/x0g8g9vtyrvWAwlbygvSVl52EA8eq4PM3vY7juSgZ7SCfZpVeik7+I1uhVZdCScmB9fL/haH7SPi2fAHf9m/1Do2IKC447LU35yqcTV/DQJIkSFZH3eJFREQUPZq6ThAB2vEDENXHG7zv8vjh8QXgSOAEFyKiWMKEcpxql50If0DgYHH47oaYLrwalkvvDlv7FDpC0+D+8mV4VrwHAJBUI8wDp8BS8BAMXUcAAAInDkKI0MyGICJqjhKsRsiShPLq4BbFlW0OCBdr3RMRRZumrhNEgOYshXSayUinxsxTT/kQEVFsYMmLOHVqYb69RyqRl50UtvMIrXYGtCTz3kQ0CxTtgnBXQc3tW+99Nat2UQyt4hhq5j0OpWU3mIfdBtmSqEeYREQxTZYlJNmNwZW8ACBZHQiU7A9NUEREFFJNXSeouRPOMihpuQ3eLz+5kG0wi/IREVHkMQsYp5ITTEhJNIW1jrK/cDuq/3U7AkW7wnYOCg3//rWArEJt1fO026XEFjDlX4vAkS2o+eARrpBMRNRESTYjyoMoeQEAckI6JMUQooiIiIj0JQI+CFflaWcoV5yaoZzAGcpERLGECeU4lpedhD1HKsPWvmRNBIQGUX0ibOeg4Akh4N+/FkpOV0hGy2n3kSQJxu4jYZ00HZI5Aa5P/wH3srchAr4IR0tEFNscdlPQM5RNF14F29VPhCgiIiIifQlnOQBAtiU32FY3Q5klL4iIYgoTynEsLzsRJyrdQddyPBPZlgoA0KpLw9I+hYZWehiiqqRBuYvTUVJawTppOgzdLoFvx/cQNeXhD5CIKI44EkxhG3eJiIhikWRLhvWqJ6DkNlyosLzaA6NBhtmo6BAZERE1FRPKcexUHeVwzVKWDCZIJjtnKEc5yWCGofsoqG0at9K0pBphHnQjbNf+FXJCOoTfA9/OpVywj4ioERw2I6pdPvgDWpPbCJTsR/Xb/wP/0R0hjIyIiEgfkqJCSWkJ2ZzQYFt5tQcOuwmSJOkQGRERNRUTynGsdYsEqIqEvWGsoyzZU6ExoRzV5MR0mAdeD9l6foszylYHAMC3/Tu4v5kJ95LnobmrwhAhEVH8OFUDsqI6iLIXBiNE9QnesCUiorjg378O7mWzITR/g23l1V4uyEdEFIOYUI5jBlVG6xYJ2HMkfAll2Z4C4a0JW/sUHK26FN6tX0N4nE1uw9DtEpguug7+Q5tQ8/4j8B/eHMIIiYjiS9LJL8XBLMwnW2trTIqa8I3fREREkeIv3Arfju8BqWFZi/JqDxfkIyKKQUwox7m87CTsP1YV1KO3Z2MeeQ9sEx8JS9sUPP++VfD88GZQCWVJkmHsMRrWSX+GZLLB9ckzcP/4LoQIz88UEVEsO7WoUFAL8xnMgGqCxjr2REQUB4SzDLItuUFZCyEEKqq9XJCPiCgGMaEc5/JyEuH1a9hfGKY6yrIalnYpNPz710JOaQk5MSPotpTU1rBeMR2GriMgaiogSfz4ICL6pVOzrIJZmE+SJEhWB4SzLFRhERER6UZzlkKypTR43+0NwOMLIMnOkhdERLGGGaE4l5ddWzd3x4HSsLTvP7YTzg//jEBZYVjap6bTXJUIHNsJNbdvyNqUVBPMg6fCfPE0ALX10Ko2fh2y9omIYl2C1QBZklARRMkLAJBtDggXS14QEVHsE84ySLbkBu+fuvnKGcpERLGHCeU4l5JoQpLdiO0HwjPLSZJkaCcOQlSVhKV9arrAwQ2AECFNKJ8iybUfHb6dP6Dko5f4WDYR0UmyJCHJbgyu5AUA84g7YRn9u9AERUREpBOhBSBqyiGfNqFcO1YyoUxEFHuYUI5zkiQhLzsJO8KVULanAgA0rkQfdfz710Kyp0JObRO2c5gGXA0IDb7t34XtHEREscZhNwZV8gJAba1JoyVEEREREelECJiH3Q61bf8Gm36aocySF0REsYYJ5WYgLycRR084UVkT3Gyp05EsSYCkQFSHp6QGNZ2hx2iY8ic3WPwilOSkTFhye8C3/VsIjYv0EREBQJLNVDfrqqn8R7bCteR5CK8rRFERERFFnqSoMHQcBCWt4SSXCs5QJiKKWUwoNwOn6ijvPRL6hfkkWYZkT+YM5SikZneGod0FYT9PQt9RENUnEDi8KeznIiKKBY4EU9AzlIWrAv79a6HVcGE+IiKKXYGyQvh2fA/hczfYVl7tgdEgw2xUdIiMiIiCwYRyM9AmMwGKLGFPYXgW95HtqRBOzlCOJt5Nn8G3Z2VEzmXreAEkSyJ8e1dF5HxERNHOYTei2uWDP9D0JzckqwMAIJzloQmKiIhIB4HDm+H+9l9AwN9gW3m1Bw67KaxPVBIRUXioegdA4WcyKGibnYg9R8KTUDYPuQVQWfcqWgjND8+aBVBz+8KQd2HYzycpBlgKHoSc2CLs5yIiigWnHt2tqPYiNcncpDbkUwllLnpKREQxTHOWAooRMNkabCuv9rLcBRFRjOIM5WaiU5sU7DtaBU0TIW9bdmRCtqeEvN1YI4SAb98a3etdBgp3AN4aqLl9I3ZOxZENSVYgAr6InZOIKFqdWlwomLIXEhPKREQUB4SzDJI9+bSzkGtnKHNiEhFRLGJCuZno3CYZHl8AR447Q952oGQfXN/8C5or9DWaY4l/5w9wf/4CPCs/0DeO/WsBxQi1ZbeIntez7iM433sQQgtE9LxERNEmyVY72yqYhfkkowUwmKGx5AUREcUw4SyDbGs4+UgIgQrOUCYiillMKDcTndrUDuLhKHshXFXw7/weorI45G3HEuFxAooRvh3fQasJT3mRc8YgBPwH1kJt1QOSGtlfzuTkLIjqE/Af3BDR8xIRRRtHwqmEcnAL81mG3wFD52GhCImIiEgXmrMUki25wftubwAeX4AJZSKiGMWEcjORmWpFgtUQloX5pJPlLrTqEyFvO5YYe46B9cpHgYAfvs2f6xKDVrIPwlkW0XIXp6ite0OyOuDb9k3Ez01EFE0SrAbIkhR0QlnN7QMlJSdEUREREUWeod2FUFv1bPD+qTEyiSUviIhiEhPKzYQkScjLTsKeI6EvSyHbUwEAopkmlP1HtsKzbhFEwA/FkQ21bT94t3wJ4XNHPBY5tRUsY38PtU3viJ9bkhUYOg9F4NAmaFUlET8/EVG0kCUJSXYjKoIoeQEA/sJt8Op0g5KIiCgUTAOugaF9foP3T5WF4gxlIqLYxIRyM5KXk4hjpTWodoV24TTJaAGMFmjVpSFtNxYIvxfu79+Eb8cPgNAAAMb+V8A69veQDOaIxyMpBqitekI6zSrKkWDoPAyQAN/273Q5PxFRtHDYjUHPUPYfWA/PyvchROgX1CUiIgo34XEicOIghL/hDdZTYyQX5SMiik1MKDcj7bKTAAD7joZnlnJznKHsXf8RRGURzENugqTW/jKkJGdDyewAABAnk8yRoJUfg2vJCwiUF0bsnL8k21Oh5uUDEj9aiKh5c9hNQSeUZasD8HsBnys0QREREUWQ/+h21Hz4Z2hlDb+f/JRQ5gxlIqJYxKxPM9I2KwGSFJ6F+Uz9r4Shx6iQtxvNAmVH4F3/MdQOA6HmdK23TXicqFn4VETrCfv2r4F//5qIL8b3S5YRd8DUf5KuMRAR6S3Jbqp7nLepJJsDAKDVlAcfEBERUYSJ6jIAOO2ifBXVXpgMCsxGJdJhERFRCDCh3IyYjSpaptuxpzD0M5TV3D5Qs7uEvN1oJYQGz/dvAgYzTPmTG+5gtEIE/PBu+BRCC0QkJv/+tZDT29bVtNaT8LnhP7Be7zCIiHTjsBtR7fLBH2j6kyqS1QEAEM7y0ARFREQUQcJZCsgKJEtCg23l1R4k2Y2QJEmHyIiIKFhMKDczedmJ2FtYCS3E9Ri18mPwbv78tPWx4pLXBSgGmAdcC9mS2GCzJEkw9hkPUVUC/96VYQ9HqymHVrwHaps+YT9XY3g3LYHrs+egVRTpHQoRkS5OPcIbzMJ88qmEMmcoExFRDNKcZZBsyZB+UQ7P5fFjx8FytEi26hQZEREFiwnlZiYvJwkujx9HT9SEtN3A8X3wLJsNrfp4SNuNVpLJBsu4+6F2GnLGfdQ2fSA7suFd/3HYF1Ty719be87cfmE9T2MZOg0BJBm+7d/qHQoRkS5OLTIUTB1lyZYCQ8+xkB1ZoQqLiIgoYoSzFLItpcH7C5fuQ6XTi4mD2+oQFRERhQITys1Mu+za2bR7Q1xHWTpZZkFUl4a03WjkWfkB/IXbIEnSWR/RkiQZxt7joJUeRuDQhrDG5D+4EVJSC8jJ2WE9T2PJtmSobXrDt+N7iIBP73CIiCLu1AzloBLKBhPM+ddCSecXbiIiij2SLQXyL8awwyXV+HzVYQzplV333ZSIiGIPE8rNTIsUK2xmFXsKQ5tQPlW3V6s+EdJ2o43/8GZ413+EQOH2Ru2vts+HnNEOwusOa1yWS++GZdRvo6oGmaHLxRDuKvj3rdE7FCKiiEuqSygHVwoqUHoYgZL9IYiIIkGrKQ/7U0lERLHCMuIOmC+6ru61EAKzl+yExaTgymHtdIyMiIiCxYRyMyNLEtplJ4V8YT7J6gAkCSKOE8rC74X7+zchJWXC2Ht8o46RZBXWif8LQ/v8sMYmqUYoUTI7+RSlZXdICWnwbftG71CIiCIuwWqALElBzVAGAM/St+BZ/naIoqJw0qqOwznrd/CuW6R3KEREuhNCg9DqL0y7YmsRdhwqx5UX5yHBatQpMiIiCgUmlJuhvOxEFJY4UeP2h6xNSVYgWZPjeoayd+1CiKoSmIfcBElt/C9AkiRBq6mAb8f3YYnL/e2/4V46KyxtB0OSZJjyr4OxzwS9QyEiijhZkpBkNwadUJasDmhclC8mBEr2AgC8axZwljIRNXta6WFU/+t2+A+sB1C7EN97X+9G26wEDO0ZXRNhiIjo/DGh3Azl5SRBANh3LLSzlA1dh0PN7hrSNqNFoPQwvBs+hdpxENTsLud9vG/7t3B/+y8EThwKaVzC74Vv70pAC93NgVAytO0HtWV3vcMgItKFw25ERZAlLySrA8LJMgqxwNDuQpgGXAuIAAJFu/QOh4hIV8JZCggNkiUBALDgh32orPbihlGdIMvRU6aPiIiahgnlZqhtViIkAHtCvDCfqU8BDB0HhbTNaCFclZCTs2DKn9yk443dLgEMZnjXfxzSuAKF2wCfG2pu35C2G0qB4r1wfTEDwh9cUoWIKNY47KagZyjLNgcQ8ALemtAERWEhNA1CCBi6jgAMFpZ7IqJmT6suA1C7MN/hkmp8sfowhvbORtssLsRHRBQPmFBuhqxmFVlpNuwNcR1l4a6G/+gOCKGde+cYo+Z0hfXKv0A2JzTpeMlkg6HLcPj3roBWWRyyuPz71wAGM5QmzJqOFOFzw793Ffz7VusdChFRRNUmlIOdoZwMANBqQnsTmELLv38NnG//D0RNOQydBgMnE8xERM2VcJYCkgyYEzGrbiG+PL3DIiKiEGFCuZnKy07EniMVIf2y49v9I1yL/grhqgpZm3rTasrh/u7f0FyVkKTgHs0y9hgFSAq8Gz4JSWxC0+A/sB5q616QFENI2gwHJbszpKQWnK1FRM1Okt2IapcPPn/Tb7TKiRlQcroBYHIymvkProfwuSElpMJ00fWwXHJn0L83EBHFMs1ZBsnqwIrtJdh5qBxXXZwHuyV6v7MQEdH5YUK5mcrLSYLT7UdRmStkbcr2VACAiKOF+TzL34Fv5zLAE/yjxrItGYaOg+HbuRTC4wy6Pa3iGITXFdXlLoDaxfmMnS9G4NhOBEqP6B0OEVHEOOwmAECFs+llL5SMdrCOfwBKck6owqIQE5qGwMGNUFv1hCSrkCQJQgvAf3gzZykTUfPlcUJYHZjz1W60zUrEkF5ciI+IKJ4wodxM5WXX1q4KZR1lyZ4CANDiJKHsP7QR/j0rYOxTANmRGZI2jX0LYJ00HZLJFnRbSnI27FNfhNqmTwgiCy+102BAVuHb9rXeoRARRUxdQjnIshdC80P4gqvFTOGjleyFcFdBbdO77j3/nhVwffIMF+cjombLMvq3+CThGlQ6vbhhVEfIfGqDiCiuMKHcTGWl2WAxKdgTwjrKP81QLg1Zm3oRfg/cP/wXclImjL3Hhaxd2Z4KJaUlhBAQAV/T4xMCQvNDMpggqcaQxRcusjkBarv+8B/cEJc1tomITsdhr/18DnZhvuo3fwPPqg9DERKFgf/AekCSobbqUfeemtuvdnG+rbyRSkTN0+Hiany+5iiGcSE+IqK4xIRyMyVLEtplJWJvCGcow2QDVFNczFD2rlkAUXUcpiE3h7w+sfB5UDP3zyhfvqDJbWhlh1H933vhP7I1hJGFlyn/OtiufhKSxI8dImoeTs1QDn5hviSImvIQREThoDnLoGR1qvf0kWQwwdBhIPz7VkG4q3WMjogo8jSPE94Fj6G/9TCu4EJ8RERxiZmdZqxddhIOlVTD4w2EpD1JkqC26gHJmhSS9vQkhAZD52FQszuHvG3JYIJkdaBi1ccQ/qbNWvPvXwt4XZCTY6cWmWxNgqQaIbyhq9tNRBTN7FYDFFkKeoayzIRyVLMMnwbLuP9p8L6hy8VAwA/frqWRD4qISEfrN+xGuijBwG7pXIiPiChOhTWhvGjRIowbNw6jRo3C7NmzG2x/8cUXMXz4cEycOBETJ06s26ewsBBTpkzBmDFjcNddd8HpDH4BM2ooLycRQgD7joau7IVl5K9h6j0+ZO3pxZw/GaYhN4etfWPvCdBqKuHb/n2TjvfvWwu5RR5kqyO0gYWZ//BmVL/1WwROHNQ7FCKisJMlCYk2Y9AJZcmaDI0J5agkvDUQQkCS1QbblNRWkDPy4Nv2LRfnI6Jmo8btx/KV2wAAXbq00zkaIiIKl7AllIuKivDss8/i7bffxvz58/Hee+9h9+7d9fbZvHkz/vnPf2LBggVYsGABpkyZAgB47LHHcP3112Px4sXo3r07XnrppXCF2ay1y66dSbynMIRlLwAInzuk7UWSb8f38G79CkJokMK4cISS2RGmlp3g3fgphOY/r2O1quPQThyAIbdfmKILHyUtF4AG37ZvdI6EiCgyHHZT8CUvbA4IZzmTklHI9cVLcH3yzBm3m/pOhLH3eIDXjoiaiQU/7IPBW/v9UrEn6xwNERGFS9gSysuWLUN+fj4cDgesVitGjx6NxYsX19tn8+bNePXVV1FQUIDHH38cHo8HPp8Pq1atwujRowEAV1xxRYPjKDTsFgNapFix50joZih71n+M6v/cCeEP7suzHjRnGdzL3oZ/32oA4V2FWJIkOAZeAVF9Av7dK87rWP/+tQAANbdPOEILK8lsh9ruQvh2LYvpGw9ERI3lsBtREXTJC0ftAqxNLJNE4SG8LgQKt0NObXXGfdTWPWHoOAiSzCpzRBT/DhVX48s1h9E7RwFQ+4QNERHFp7D9dltcXIz09PS61xkZGSgqKqp77XQ60aVLFzzwwAOYN28eKisr8dJLL6GsrAx2ux2qWvvoYHp6er3jKLTaZydib2FFyGY9ySfrJwtnWUjaiyTP8rcBzQfz4JvCOjv5FGv7vlDb9AFU43kdJ1yVkNPaQE7KDFNk4WXsMhzwueHb/aPeoRARhV0oZigbeoyG/aYXIRnMIYqKQsF/eDOg+WvH8rPQKorg/u7fXJyPiOKaEAKzluyA1ayiS7oEyZIISWlYDoiIiOJD2D7hNa1+yQAhRL3XNpsNM2fOrHt966234uGHH8b111/fIJl3vsm91FR7E6NuKD09IWRt6e10fenVKQNLNx+DpijITLWd5qjz43K2xFEAiaoLljD93YXjmtTsWoOqvauQPOw6JLdvH/L2z6T1DY+c/0Hjb4HQApBkJfQBNdH5XBOR1geHl7eG2PU90ocWhDGqpomXf/Px0g8gvvpCzY/DbkS1ywefX4NBbdp9/Ejc5KTz5z+4HjDZoLQ4++8Nwu+Fb/t3kB05MPYcHZngiIgibPmWY9h1uAI3j+0MW4ceEDWX6h0SERGFUdgSypmZmVi9enXd65KSEmRkZNS9LiwsxLJly3DVVVcBqE04q6qKlJQUVFVVIRAIQFGUBsc1xokT1dC04GfcpqcnoKSkKuh2osGZ+pKRaAIArNpUiPxuwc941QJWAEDZ4UOotuUG3d4vheOaCJ8Hzk9ehZycDV/7SyJ2zU/1RassgW/vShh7jTtn0kBzVUIy2aIumXy+f2dy5xEIHN2B4qMnah/jjhLx8m8+XvoBxH5fZFkK6U1Oij1J9tpxtsLpQVqSpUltaM4yuD79B4x9J8LQ7oJQhkdNJDQNgYMbobbqcc4x+afF+b6Gocco3iAgorhT4/Zjztd70C47EYN7ZkGWJCDGFg8nIqLzE7aSFwMHDsTy5ctRWloKl8uFJUuWYOjQoXXbzWYz/v73v+PQoUMQQmD27NkYOXIkDAYD+vfvj08++QQAMH/+/HrHUWjlpNtgMijYUxiaOsqSrbZOllZdGpL2IkG4qyBZHTANuVmXx7L8hzfBu/J9BI7uOOe+nu/+g5r5j0cgqvAydrkYlhF3RFUymYgoHBwnE8rBlL2QDGZopYehVZaEKiwKknBVQEpIO2e5i1OMXS6GVnEMgWM7wxwZEVHkzf9hL6qcXtw4qhNkSYL7h//WPsVBRERxK2zZsxYtWuC+++7D1KlT4fP5cNVVV6Fnz56YNm0a7r33XvTo0QOPP/447rrrLvh8PvTt2xe33HILAGD69Ol48MEH8fLLLyMrKwv//Oc/wxVms6fIMtpmJWDPkYqQtCcpBki2FAifKyTtRYKckAbrxEd0mzFk6DgY3jXz4V3/EdTszmfcT/g88B/eDEPnYRGMLnxEwA///rW1s7uMTZu1R83LqVrvkiRBBPwQ3hog4Ac0P6AFIDQNksEIOSEdQvMjULQH0AJ1/wkRgCTJdQkg395VEO6qk9s1CC0AaH4YulwM2ZKoZ1cpjjjstTfOglmYTzJaAIMZoqY8RFFRsGRbMmyTpjd6fzXvQmD52/Bt+xpqVqcwRkZEFFkHi6rw5ZrDuLhPDtpkJkD43PBt/QqSPQVq6956h0dERGES1umYBQUFKCioXyP153WTR48ejdGjG9aSy8nJwVtvvRXO0Ohn8nKSsHjFQXh9ARgNwZdSsF3/DCQp+lczF5oGz/dvwNBtBJS0XN3ikFQjDD1GwbvyAwSO7z9jLP7Dm4GAD2rbfpENMEy0Ewfh/vIlmAZPhbHrCL3DiStCC0D4vYDQTiZaA4AQdYtmalXHT26vTaRCCwBCg5zSCpLBhEBZIURVSW2S9dQ+IgA5rQ0URza0ymL4D22se1+cTMjKSS1gyBsA4ffAs2pu3flPJWsl1QDz4KkAAPfSWdAqi2uTwQE/hOYHAgFYLr0LclImPKvnwrf9OzhFAJrfd3K/AEwDr4ex+0j4D6yD+4sZDfqu5vaDZdRvAK8brkV/bfiXY7Ih4aba47xrFkArO9ywjTZ9ACaUKURCMUMZACSrgwnlKKJVHIOU2KLRN6Ml1QRD+4Hw7VoK4fdAUk1hjpCIKPyEEJj1+U7YzAZMGtqu9r2Ti7PLthQ9QyMiojDjsquEdtmJCGgCB4qq0KGlI+j2YiGZDADedQvh2/EdlJwuuiaUAcDYdQS86z6Gd/0nsFx692n38e9fU7v4T2bHCEcXHnJ6W8iprWtrSnYZzpqSIeI/thP7XvsrgF/UkTdakHDzywAA15L/B+3EoQbHWq94DEpaG/i2fQ3f5s8bbDdddB0URzYCpYfgWTqrwXa1TR8Y8gYAWgC+7d8CklxbW1SSAVmBZLLW7StclbXlZmQVUNTa8ieyWrsvANmRDbV1L1hsFri8orYdRYWS3hYAoKS1gWnQDYCs1paqkRVAVn768mI0wzL+D7XnPbkNkgwohroYLOPvr/3DqX0kpa4dolCxWw1QZAnlQcxQBgDZmsSEcpTQKkvgfO9BmAbfBGPX4Y0+zti3AMZ+E5lMJqK4sWzzMew+XIFbxnaG3VL7O5Z2MqF8qhQiERHFJyaUCXnZtbMW9xypDElC2bvlS/i2fgXrVU9EbZLQf2AdvGvmQ+04CGpevt7hQDJaYew2At6Nn0FzVTZ43F5ofvgPboDaundULcgXDEmSYOgyHJ4f3oRWshdKRp7eIcU04fNAMpigZnZE+sR7UXH0CCRJAWS5NlGq/pRINV1wdW1ZmlOJ3pP7yIm1C6Aae4yCof1FJ7fXbpNkBZIlAQCgtuwB243P10sW150HtT/PCbe8ctZ4z3Tj5BRD+3wY2ucj7QyL8smJGTB2O/Pq4ZKsQs3petZzyFwshiJAliQk2oxBJ5QlazICJXtDFBUF41Rd0HN9xvzSqc8cEfDXfvZG6e9IRESNUeP24f2vdyMvOxGDembVvS+ctWvpcIYyEVF8Y0KZkGgzIt1hxp7C0NRRhuaHVnYEwlMNyZwQmjZDSCs/BtdXr0FOawPz4Jui5gudoecYGDqfvnarcFdDScuFoV1/HSILH0P7fHhWvAfv1q9hiYGEshAatPJjkBPSompBQa2yBDWfPgNj10tg7DEKCd2Hwt2iYRL2FLV1z7O2JyekAwnpZ9wuqcao6j9RtHPYTUGXvDANuBpAdIxXzZ3/wHrIjizISS3O+9jA8f1wffpPmC+5+6zrJhARRbv53+9DlcuH+67pDfln36dOLc7OGcpERPGNCWUCUDtLefvBMgghgk6wSvZUAICoLgWiLKEsAj64ljwPSVFhGfmbqEqKyeYEwJxQW4824INkMP+0zeqAdfwDOkYXHpLRAkNePny7lkFcdB0kk03vkBoQfg8Ch7fCf2Ad/AfXQ7gqz/sx53AKHD8A16f/gNACdeUgiCi6OOxGFJcHt1itfHJsJX0JrwuBo9th6D6qScfLjiyIgA++7d8woUxEMetgURW+XPvTQnw/p7buBcmcEFXfs4iIKPSYUCYAtQvz/bi1CGVVHqQkms99wFmc+tKrVZ+AktYmFOGFjKQYYOw9DpItBXJCmt7hNCACPjg/+F+orXvBfNF1te8JAa14D+SMdjFTn/p8GHqMgtKyG2CIvpqS3k1L4Fn5PhDwAQYL1FY9oLTIg6HLMACAVn4UsiPrHK2Ej//wFrg+fwGSyQbrhAehJGfrFgsRnZnDbsKuw8E9BRQ4cRDeTZ/B1O/y2qcISBf+w5sBLQC1Te8mHS+pJhg6DIRv27fQBlbV3kwmIooh2s8W4rvi5EJ8P6ektYm674BERBR68ZedoiZpl11bZmFPYWXQbf00Q/lE0G2FUqCsEABg6Dj4vOseRoqkGKCkt4Vv2zcQ7moAgHZ8P2oWPAH/7h91ji48lORsGNpdULs4m06EEAiUHkbZ0g/hnP84fHtXAQDk5GwYOg+DZdwDsE99AZZL74axx2hIkoxA8V443/8T3D/8FyLgi3jMvv1r4Vr8T8gJabBOfITJZKIo5rAbUe3ywefXmtyG8NTAv3MptMqSEEZG502SoWR1htKifZObMHS5GND88O9cGrq4iIgiZPnJhfiuHp4Hm9nQYLtv948IFO3WITIiIookJpQJANAqww6DKmPLvtKg25LMCYCiQouihLL/0EbUfPAn+HYv1zuUczL2Hg/4PfBu+RIA4N+/FpBkqK3OXvc2lmk15XB9+Qr8x3ZF9LyB0sNwL5sN57t/QM0Hj6Dsm7cBIWoXmQOgtuwO86AboLbsBkmpn/CW01rD0GMUfFu/Qs3Cp6BVRTbJo6S0hJrbF9aChyCzRh1RVHPYa5/AqHA2fWE+2eYAAAhnWShCoiYytO0Ha8GDQS2Qq6S0gtyife3NYyFCGB0RUXjVLcSXk4hBPU7/lJ5n2Wz4dv4Q4ciIiCjSmFAmAICqyLigcwa+21CID77ZAy2ILziSJMF6xeMw9Z0YwgibTqsshuurVyEn1ybgop2S0hJK697wbf4cwueBf/8aKFmdIJnteocWNpLBAv/BDfBt/Sqs5xEeZ+2sieK9AACtvBC+bd9ATs6GacjNaH3vTNgmTYehET8nkqzCnD8Z5lG/gVZxDM4Pp8N/YF144xcavJuWQPjckBMzYLn0nqisO01E9SWdTCgHszCfZHUAALSaEC2gS+dNqz4BrfxoSNoydhkOyZ4CeJwhaY+IKBLmnVyI74aRneotxHeK8Hsh3FWQbCk6REdERJHEGspU55ZxnWFQZXzy4wEcr3DhtvFdYFCbNgMnWh6/Fz4PXEteAABYRv0Gkhp9dXpPx9RnAmoWPAHPiveglRXC1CU6FoALF8lwsqbkjm8h3FNCljwXmh+i8jj8hzbCf2AdAkd3AiIAQ9cRUDLaQW3TB/apL0I6Wb9ZTUgA3FXndQ5Dbj8oV7SC64sZcH/7b9gmPw3JaAlJ/PX6EvDD/e2/4N+9HFBUGLuOCPk5iCg8HPbahYnKq5o+Q1kymAGDGaKGM5T14tvyJbwbP4P9pheD/pxXOwyEoeOgEEVGRBR+B4uq8NXawxh+moX4ThE15QDAp+eIiJoBJpSpjiLLmDq6EzIcFrz/zR6UVnlw75U9Ybc0rI11Lr49KxA4sgXmobeGIdLGEULA/d2/oZUehmXs7yEnZugWy/lSWrSHocvF0MqOAEBMzKwOlqHrxfBt/RK+nT/A2HPMafcRAX/trAeTDZJqRKBkPwJHd0C4KyFcVRDuKmiuShg6DYGxy8UIHNkK16f/BFBbD9nYawzUNn0gp9cuICIpBqDpTy3XkRMzYL3sT9AqiiAZLRBeV+0s4hD9Mi28Lrg+fxGBI1tgvOBKGOL8BgNRvHEknCp50fQZykDtLOVTX9Yp8vwHN0DJ6hiSm4aSJNXW7z+2E0pyTlw/hUREsU3TBDbsPo4Pv9sLu8WASadZiK9u35NlmThDmYgo/jGhTPVIkoSx+W2QmmTG6x9tw5P/XY3fXdMLLZKt59WOVlYI3/bvYRo8VbfF1oS7CoHivTBeeCXUVj10iSEY5iE3w7d3FeSkLMgnFzqMZ6dqSnp+fBeG7qMgyTI8qz6Ev3DbyWRxJeB1AQAs4+6H2rI7/Ic3wbvqQ0BSIFkSIJkTIFkSIam1swHllFYwDb0FalZnyEktwhq/pBqhpLYCALiXzULg4EaYR9wJtWW3oNrVairgWvxPaCcOwTzsNhg6DQlFuBTHFi1ahJdffhl+vx833XQTpkyZctr9/vCHPyA/Px9XXHFFhCNsfuwWAxRZQnl102coA4B5wLUAE4+60CpLoJUdganz0NC1WX4UrkV/hSn/Whh7jg1Zu0REoVDp9OK7DYX4dv0RnKj0IDnBhNvGdzntQnynCGftejySnTOUiYjiHRPKdFoXdmkBh92EFz7ciCf/uwb3XtUT7XOSGn28ZE8BICCcZZAS0sMX6FnIlkTYrnwMMIS+/ECkGNpdAEO7C/QOI2KMPUbBXbQb8NYAZjtEwAdJNUJOz/0pWWxOgOyoXQTE2O3S2tIPRiuk09Rxk23JMHYeFuluwNhrPNwl++D65BkY+10OY98CSFLTStb7Nn0GrfwoLKN/C7V1rxBHSvGmqKgIzz77LObOnQuj0YjJkydjwIABaN++fb19pk+fjuXLlyM/P1/HaJsPWZKQZDcGVfICANTcPiGKiM6X/+B6AIDaJnTXQEnOhtyiPbzbvoWhx5jTjmNERJEkhMCeI5X4au1hrNpejIAm0KVNMiZf0hG9O6RCkc/++6xkT4Wh0xDInKFMRBT3mFCmM+rYyoFHpvbHs3M24Om31+FXBV3Rv3PjykacmlGrVZdCjnBCWas6Ds+y2TANuQnyyUWMKDYY2l0I9ZZewMkZxub8yWfdPxy1ikNBSc6G9fLpcH//Brxr5iFQtAvm4b+CbElsdBvC74WkGmG84AqoHQZCSWkZxogpXixbtgz5+flwOBwAgNGjR2Px4sX49a9/XbfPokWLcMkll9TtQ5GRZDOhPMiSF4HjBxA4thOGbpcy+Rhh/gPrITuyQ14+y9jlYri/eR2Bo9uhZncJadtERI3l8Qbw49Zj+HrtERwsrobFpGB4nxwM75uDrNTGLwCtZnaEmtkxjJESEVG0aNqUOWo2WqRY8fDUfmiTacdL8zfj0xUHIIQ453GnEsqi+kS4Q6xH+L1wff4C/IXbAa87ouem0JAMprhIlEgGE8zDfwXTkJsROLodvo2LG32s/9BGON95AIHjByDJKpPJ1GjFxcVIT//pJl5GRgaKiorq7XP77bfj6quvjnRozZ7Dbgy65EWgcBs8y2bXPsVBEaVkdoQhDIuhqu0uAIwW+LZ9G/K2iYjO5VhpDd7+Yid+P2Mp3ly8A5oApo7phH/cMwjXj+x4XslkAAiUFUKrLg1TtEREFE04Q5nOKdFqxAOT++D1j7fh/a/3oKTcjSkjO5z1kafakheI6C8UQgi4v38T2vEDsIz+HWRHZsTOTXQ6kiTB2OViKC3yICfW1nAOlBdCTso6Y9Lct3Mp3N/+G3JKNiRr48vMEAGApmn1fraEEGG5QZOaGro6vunpp18pPtacqx9Z6XbsPlIZVH+rMzNRDMBh9sOYFr6/t2i+JjX7NkB4PbB1uvCc+4a0H2NuCF1b9SRA7jkcleuWIMUmoFhP/yRLNF+T8xEv/QDipy/x0g9qvICmYcPuE/hq7WFs3V8GRZbQv3MGRvTNQfucpKB+b3B/MxOSyQbruPtDGDEREUUjJpSpUYwGBXdO7IYPk8z4dMVBlFa6cefEbjAbT/8jJKkmmEfcATmtTcRi9G35Ev5dS2HsdznUNr0jdl6ic1FSahfr06pPoGbuY1Bbdod52K2QTD/N+hBCwLvhU3hXzoGS3QWWUfdGbUkPil6ZmZlYvXp13euSkhJkZIT2EX0AOHGiGpp27qdVziU9PQElJVUhiEhfjemHSZFQVeNF4dEKGNSmPSDm95sBACcOH4EqwnPDKVqviW/nUgifC76dyyCqj8Nma13vM/SXQtkP/6FNkJNahLzcxSmBdoNhMqbgRLkHkrNhzNF6Tc5XvPQDiJ++xEM/ZFkK6U3OeFbxs0X2Sk8usjdpaDsM7ZWNJJsxJOcQzrK633uJiCi+seQFNZosSbh6eHvcOLoTNu09gb/NXouysywwZGh/ERRHdkRiC5w4BM/yd6C07g1j38sick6i8yXZUmDqPwn+A+vhnPsoAscP1G3zrJgD78o5UPMGwDL290wmU5MMHDgQy5cvR2lpKVwuF5YsWYKhQ4fqHRYBcNhNAICKIMpenFoXQDjLQhFSzNDcVXAvfxv+vatgHjwVwlUFz+p5ETm30DS4v3o1rOdTHNkwdr8UksEctnMQUfMkhMCuw+V4deEW3D9jKeZ9txeZKVb8+ooeePqui1AwMDd0yWTND1FTAcmWHJL2iIgounGGMp234X1ykJpowsvzt+CJ/67GfVf3QsuMhjMD/Ed3wL9zKSRbMgydh0G2h2+1XzklB6b8a2DoNASSxPskFJ0kSYKx5xjIGXlwf/kSahb8BaaBN8DQeRiU9Fygx2iY8q/lzzA1WYsWLXDfffdh6tSp8Pl8uOqqq9CzZ09MmzYN9957L3r06KF3iM2WI6E2oVzu9CLN0bQbRqfK4Gg1FSGLKxZ4V34AeF0wDZoKJSUHhq7D4dv6JQydh0JJbR3WcweKd0N4qqG26RPW8wh3NTzrFkFt2x9qZoewnouImocftx7DklWHsf9oJSwmFcP75mB4n/NbZO98iJoKAIIJZSKiZoIJZWqSnnlpeHBKX/y/DzbgqVlrcM+kHujWtn7CWDLboTlLEdjxPbzrFkJt3RuGLsOhtOwO6Sz1l8+HCPignTgEJaMdjD1Gh6RNonBTMzvAesVjcH/9GjzLZkFt2Q2GvAEw5A3QOzSKAwUFBSgoKKj33syZMxvs97e//S1SIRGAzBQrJAALf9iH31zZs0llLySjBYZul0BJbT4LdQaK98C3/TsYeo6GkpIDADBdcCX8e1fB88NbsFz2cFgXcg0cWA9ICtRW3cN2DgCAaoRv5w8QNRVMKBNR0PwBDbOX7ERGihU3jemE/K6ZMBmVsJ5TnFw7R7aFbxIRERFFD06DoyZrk5mAR6b2R1qSGc+9vwHfbyist11JzoF13P2wTf4/GHuNQ6B4D1yL/wnP0v+GLAbP0lmoWfgktMqSkLVJFAmyJRGWMb+HdeIjkBPS9Q6HiMIs3WHBTWM7Y/O+UryyYDP8Aa1J7ZgH3Qi1Vc8QRxedhKbB/cNbkKxJMPWdWPe+ZLLBdOHV0KqPQ1SfCGsM/gProWR3gmS0hvU8kmqEocNA+PethuaO7Zq2RKQ/VZHx/+4dgv/3+4sxrHdO2JPJACAgIKe1gZSQFvZzERGR/phQpqCkJJrx0A390Lm1A//5dDvmfrcHQtRfqElOzIDpwqthu/6fMF96NwydhwGo/ZLmWvIC/Ic3Q4jz/2Lt3fYNfNu/hbHnWMiJTMhR7JFkGUpart5hEFGEDO2Vjesv7YB1u47j9Y+2NmlhQ626tF799XgmPNWALMOUP7lBXXm102DYrvkr5DAmLrTKYmjlhVBb9wrbOX7O0OViQPPDv+OHiJyPiOKbLEthfYLjl9TMjrBd8RiU5MisoUNERPpiyQsKmsWk4rdX98KsJTvw0bIDOF7uxi3jujR4nFdSVBjaXVj3WrirEDi6A/79ayAlpMPQ5WIYOg2BbEk85zkDRbvhWfoWlJbdYex/Rcj7REREFA6X9m8Fn1/D+9/sgUGVccu4LpDP4wu/Z+UcBIr2wH7d38MYZXSQLYmwTnwEQMO/H0mSAYMZWk05Ake2wtBhYBgCUGHsNQ5qm76hb/s0lOQcKJkd4d3+DQw9x0Q0EUREFCyhBSDJ4Z8JTURE0YEzlCkkVEXGTWM648ph7fDj1iL84911qHb5znqModMQ2Kb8E+YRd0C2p8C78n04Z98H/9EdZz1OqymH6/MXIdlSYBlxZ8jqMRMREUXC2Pw2uGxQLpZuOobZn+9s8GTP2UhWB0RN+XkdE4u8Gz5FoGQ/JEk+a2LVu/4TuL+ZGZZZ27I9BaYB10T0KShD52EQFcXQSg9F7JxERKHg/uoVOOc+qncYREQUIczEUchIkoTxF+XiV5d1xd6jlXjqrTUoLned/RjVCEP7i2AteAjWq5+CscdoKOltAQCe1XPh3fhpg1qCWmUJIMmwjLoXktketv4QERGFy8TBbTF2QGt8vfYI5ny9u9EJYtmaDAR8gLcmzBHqJ1C0G54V78G358dz7mvqNxGSyQ730reaVD7rTIS3Bt7Nn0NzVYaszcZQ210A23V/h5LaOqLnJSIKluYsa1CeiIiI4hcTyhRy+V0zcf/kPqiq8eLJ/67GvqON+zKmJGfDNOAaSKoRQggEivbA8+N7cM66D66vXoH/6A4IIaBmdoBt8v9BSW0V5p4QERGFhyRJuOriPFzStyU+W3kIC37Y17jjrEkAap/WiUdC0+Be+hYkq6PeQnxnIplsMA24BlrRbvh3Lg1ZHP5Dm+FZNhtaRVHI2mwMSTVCTkiDEBpE4OxPehERRRNRXQrJlqJ3GEREFCFMKFNYdGzlwMM39oPJoOD/3l6LjXvObxV2SZJgHf8ArFc9AUOXYfAf3ADXor/i6Fv/C+H3QlIMYYqciIgoMiRJwnUjO2BIzywsXLofHy/ff+5jbMkAAOEsD29wOvFt/wba8QMwXXRdo2e6qR0HQW7RHp4VcyA8zpDE4T+wDpLJDiUjLyTtnQ/hrYHz3T/Ct/mLiJ+biKgphKZB1JRDPjlGERFR/GNCmcImK9WGP93YD5kpVjz/wUZ8v7HwvNtQUlrCPOhG2G94DuZht8HcuhvAxR6IiChOyJKEm8Z0Rn7XFvjw2734fPXZa+fKthTI6e2AOFw/QHNVwrPqQyjZXaD+bBHfc5EkGeZBN0J4XfAXbgs6DqEF4D+0EUrrnrqs0yAZrZBtyfBu/ybua2UTUXwQ7kpAaJDsnKFMRNRcxN+3EYoqSXYT/nh9X3Rp48B/PtmOhUv3NenLkaSaYOg0BCkXX8fVg4mIKK7IsoTbJnRBv47peOeLXfh2/ZEz75uYDtukP0PN7hLBCCNDKzsCSVZgGnTjWRfiOx0lrQ1s1/8Dhrb9g44jULQb8DihtukddFtNVbs4XxECR7frFgMRnd6iRYswbtw4jBo1CrNnz26wfe/evbjxxhtx2WWX4bbbbkNFRYUOUUaWqKkAJJkzlImImhEmlCnsLCYVv726Fy7qlon53+/Dfz/bgYAWuoVziIiIYp0iy7hjYjf0zEvFfxfvwPLNx864rxACIuCPYHSRoWZ3ge26Z6AkZzfpeNmaBKFp8O3+MagF+gIHNwCyArVljya3ESy13QWAyQbf1q91i4GIGioqKsKzzz6Lt99+G/Pnz8d7772H3bt3120XQuCuu+7CtGnTsHDhQnTp0gWvvfaajhFHhpLWBvbbXofSqqfeoRARUYQwoUwRoSoybp/QBeMvaoNv1xdixtzN8PgCeodFREQUNVRFxt2Xd0fnNsl4/eOtWL29+LT71cx9FO6vX41wdOEjNA2e9R9BeJyQVGNQbfkPrIX7q1dQvfGbJrehtrsApoE3NLqGczhIqhGGDgPh378GAWf8z24kihXLli1Dfn4+HA4HrFYrRo8ejcWLF9dt37JlC6xWK4YOHQoAuPPOOzFlyhS9wo0oSZb5JCkRUTPChDJFjCRJuHJYHqaM7IgNu4/j7++sQ1WNV++wiIiIoobRoOA3V/ZAXk4SXl24BRt2H2+wj2SyxtWifL7t38C78gP4j2wJui01ty/kFu1x4qu3mrxAn5LeFsauw4OOJViGLsMhp7aGv7pM71CI6KTi4mKkp6fXvc7IyEBRUVHd64MHDyItLQ0PP/wwJk2ahOnTp8NqteoRakR5N3yKmk+e0TsMIiKKIFXvAKj5uaRfSzjsJry2aAueemsN7ru2NzIc+s0CIiIiiiZmo4rfXdULz7y7DjPmbcZvr+6Jbrk/LXQkWZMQKNqjY4Sho7kq4Vn5Qe1CfG0vCLq9Uwv01cx7DJ5Vc2EefON5He/fvw5aTRkMXS6GJOk770JJzoZt0nSY0hOg7T8IyWQLegY3EQVH07R6Nd6FEPVe+/1+rFy5ErNmzUKPHj3w3HPP4W9/+xv+9re/Nfocqan2kMacnp4Q0vZOp7i6EIHqkrCeKxL9iJR46Qv7EX3ipS/x0g8gvvryS0woky76dUrH/bbeeP6DjbVJ5at7oU1m/P5DIyIiOh9Ws4rfX9sbT7+9Di98sBG/v7Y3OrZyAAAkqwOiprxBIiMWeVe+D/g8TVqI70yUtDZI7DcGlWs+haHzEChpuY2PZ/MSCFcFjF1HhCSWUBABP1yf/AMwmGAZ9RvIVofeIRE1W5mZmVi9enXd65KSEmRkZNS9Tk9PR5s2bdCjR20N9gkTJuDee+89r3OcOFENTTv/RcxPJz09ASUlVSFp62xcpcWAxRG2c0WqH5EQL31hP6JPvPQlXvoBxH5fZFk6601Olrwg3XRo6cBDN/SDQZHwt7fXYvPeE3qHREREFDXsFgPun9wbqUlmPPv+BuwprK2lK1sdQMAHeGv0DTBIgaLd8O34HoYeo5q8EN+ZJA+bDMmcAN/27xp9jPDWIHB0J9TWvUMaS7AkRYWx30RopYdQM+9xBI4f0DskomZr4MCBWL58OUpLS+FyubBkyZK6eskA0KdPH5SWlmL79u0AgK+++grdunXTK9yI0apLIdmS9Q6DiIgiiAll0lV2mg0P39gfGQ4L/t8HG7F001G9QyIiIooaiTYj7p/cB4lWA559bwMOFlVBsjoAWYFwxe6MBwDwH9kKyZYCU9/LQt62YrbBOvERmAbd0Ph4Dm0CRABKmz4hjydYhnYXwHrZwwAEahY+Cd++NXqHRNQstWjRAvfddx+mTp2Kyy+/HBMmTEDPnj0xbdo0bNq0CWazGTNmzMAjjzyC8ePHY8WKFXjwwQf1DjushBAQzjLItpRz70xERHGDJS9Id8kJJjw4pS9enLsJ//p4G8qqPBh/UZuYf4yXiIgoFJITTHjguj742+y1eObd9fjjdb2QfdvrMT9OmvpeBmO3SyAZw7OOgpxY+xh6oHgv5MQMSOaz1yX1H1gPyZwAJSMvLPEES0nLhXXSdLg+ex7uz1+APGk6lPS2eodF1OwUFBSgoKCg3nszZ86s+3OvXr3wwQcfRDos3Qh3FaD5OUOZiKiZ4QxligoWk4r7rumF/G4tMPe7vZi1ZGfIaocRERHFurQkCx64rg8URcIz721EcZlL75CaTHNVwrf7x9oa0CZbeM/lLEPNgifhWT33rPsJLQD/oY1QWvWEJEfvr8ey1QFrwYMwD7sN8sna0EJo+gZFRM2aZLLCeuXjUNsFv7AqERHFjuj9jZmaHVWRcfuErhg7oDW+XncEM+ZtgtcX0DssIiKiqNAi2Yr7J/cBND9K3n8MpWs/1zukJvGseB/ub2ZCVB0P+7lkWzIM3UbAt/VrBEr2n2VPCZaRv4axx6iwxxQsSTXC0GkIJEmCb99q1Cx4ElpNud5hEVEzJckqlNTWXDCUiKiZYUKZooosSbh6eHtcf2kHrN91HM+8ux7VLp/eYREREUWFnDQb7pvcDy1QirWrNqCsyqN3SOclcGwX/Du/h7HHaMiJ6RE5p6nf5ZAsCXAv/e8ZZ/NKsgw1uwuUtDYRiSlkJImL9RGRrvyHN8O9/B0Iv1fvUIiIKIKYUKaodGn/Vrjr8u7Yf6wKT721BsfLY/fRXiIiolBq3SIBqj0ZZn81Xpy7ET5/bDzNIzQN7qVvQbKlwBiGhfjORDLZYBpwLbTivfDv+OG0+7i/fwP+gxsjFlOoGHL7wXrZnwDg5GJ9q3WOiIiam0Dhdvg2fwHIXJ6JiKg5YUKZolb/zhm4f3JvVDq9ePKtNThYFNur2RMREYWKKTEFHdOAfUer8N/PdkCI6F93wLftK2gnDsJ00WRIBnNEz612GAglsyO8Gz5pMEtZqzgG37ZvoFUWRTSmUFHS2sA66c+QU1rC/fmL8G6OzVIoRBSbNGcZJJsjquvPExFR6PFTn6Jax1YOPHRjPyiKhL/NXot1O4r1DomIiEh3ks0Bq+bEZYNysXTTMXy19ojeIZ2VEAL+3Sug5HSD2jbyCzdJkgTzxbfDMvFPkKT6v/76D6wHAKite0c8rlCRrQ5YJzwIQ6ehUDLy9A6HiJoR4SyFZEvWOwwiIoqwsCaUFy1ahHHjxmHUqFGYPXt2g+1ffPEFJk6ciMsuuwx33303KioqAADz5s3D4MGDMXHiREycOBHPPvtsOMOkKJeTZsOfbuyPtCQLps9cjpfnb8aR4069wyIiItKNZHVA1JSjYFAuerdPw7tf7sKOg2V6h3VGkiTBUvBHmEfcAUmSdIlBTsyAbE6A8DihVf50g9p/YD3k5JyI1XQOF0k1wjzsVigZ7SC0ANw/vsvF+ogo7DRnGWQmlImImp2wJZSLiorw7LPP4u2338b8+fPx3nvvYffu3XXbq6ur8eijj+K1117DwoUL0alTJ7zwwgsAgM2bN+PBBx/EggULsGDBAtx3333hCpNiRHKCCQ/d0BdXX9IRG/eewJ9fX4FXFmxGIRPLRETUDBm7j4R10qOQJQnTCroi3WHBS/M3o7TSrXdoDQSOH4BWfhSSrEK2JOoaixACNYv+BtdXr0AIDcLjRODYTqhteusaV6hppYfh2/rVycX69usdDhHFKSHEyRnKKXqHQkREERa2hPKyZcuQn58Ph8MBq9WK0aNHY/HixXXbfT4fpk+fjhYtWgAAOnXqhKNHjwIANm3ahHnz5qGgoAD3339/3cxlat4sJhU3ju2Cv981EOMuaoMNu0/gf19fgdcWbcGx0hq9wyMiIooY2Z4KJSWnduavScVvruwBn1/Di3M3weuLnkX6hBaA+9vXUfPpPyE07dwHhJkkSTD2HAOteC98O76H/9AmQGgxXe7idJS0Nj8t1rfgKfj2rtI5IiKKS0LAPHgqDHkX6h0JERFFWNgSysXFxUhP/+nRwYyMDBQV/bTYSXJyMkaOHAkAcLvdeO2113DppZcCANLT03H33Xdj4cKFyMrKwuOPPx6uMCkG2S0GXDksD/9310UYM6A11u4swZ9m/oiZi7aiiIllIiJqBrTqE3AvfQuBEwcBAFmpNkwr6Ir9x6rwVhQt0ufb+jW0E4dgGnBN1CzYVLdA34r3oWR3hmXcA5DjsO5w7WJ90yGntYb7ixnwrFkQNT8XRBQfJFmGoeNg1m4nImqG1HA1rGlavRp5QojT1syrqqrCPffcg86dO2PSpEkAgBkzZtRtv/322+sSz42VmmpvYtQNpacnhKwtvcVLX071Ix3A3W1Scf3Yrpj7zW58vHQfVmwrwvB+LXHtpZ2QlWbTN9BGiLdrEg/ipS/x0g8gvvpCFDIBP3xbvoSS3g5KamsAQJ8O6Zg4uC0W/LAPbTITcGn/VrqGqNVUwLP6w5ML8fXXNZafkyQJpkE3ombudHjXLIB5yE16hxQ2sjUJ1vF/gPv7NxA4thMQGiApeodFRHFCqyhCoGg31Nw+kIxWvcMhIqIICltCOTMzE6tXr657XVJSgoyMjHr7FBcX47bbbkN+fj4efvhhALUJ5g8//BA333wzgNpEtKKc3y++J05UQ9OCn4GRnp6AkpKqoNuJBvHSlzP1oyC/NYZ2b4FPVxzE1+uO4OvVhzGwRyYKBuYi3WHRIdJzi/drEovipS/x0g8g9vsiy1JIb3ISnSJZHQAA9zcz4f5mJgDANHgqCgYNh3xwNQas/S+q1tY/Rm13ISyX3g2tpgLOWb9t2KYlEem//w8AwDnnIWjlRxvsY736KSjJ2XB//yZ8275usN00eCqMXUfAt2cF3F++DMgKzINu0G0hvjNRUltBbZ8P37avYew1FnJixrkPilGSaoT54mlAwAdJVhA4cRCSOSHqFtESQoN37UJ41y0CtNqyLbIjG7ZrngIAVP/3NxDuhuOB7cbnIVsS4fpiBvynKe1hvuRuGPIuhHfLl/AsfavBdkPXETAPnopA2RHUvP+nBttPF8Mvo9AjhlD8Pfy8lUjGQPHDX7gNnu/fgO36fzChTETUzIQtoTxw4EC88MILKC0thcViwZIlS/CXv/ylbnsgEMCdd96JsWPH4u67765732q14vXXX0efPn3Qq1cvzJo167xnKFPzlGQ3YfIlHTBmQGt88uMBfLOuEMs3H8OgHlmYMLAN0pKiM7FMRER0viSDCebhv4JW8VM5MSUtF7Ik4ZIRF+Lbhcfg9WsY1isbFlPtr3tySk7dsca+E0/b5imGrpecNmEkmWtvkKite0E6zQJ7Slpu7bmSc2DsOxFKVifIjqymdzSMzINvgi+zIyR7qt6hhJ0kSYBqhNA0uL54CfB7YBn1WyjpuXqHBqB2Aon7y1fg37sSam4/yCktAQCS5acnVIy9xkL4PA2OldTan1u13QWQHdkNtsvJtT/3Snrb0/7cKxlta9sxJ5z+38VpYrDaTKhx/hSLHjE02KcJMfy8H5GMgeKHcJYBkgTJmqR3KEREFGGSCGMxtUWLFuHVV1+Fz+fDVVddhWnTpmHatGm49957cezYMfzmN79Bp06d6vbv3r07nnzySaxevRpPPvkk3G43cnNz8fTTTyMhofGPPHOGckPx0pfz6UdZlQefLD+AbzccgRDAkJ5ZGH9RLlKTzGGOsnGa4zWJdvHSl3jpBxD7fYnXGcocZ+uLxn4cPeHEE/9djYxkKx6a0hdGQ+Oe9orGvjRFvPQDCH1fAicOwfXZcxCuKpiH3w5Du8jMGD1XPzzrPoIkKzD0HBN1M9p/KV5+vuKhH/E4zoZqjAXCf43d3/4L/kObYL/hubCdA4iPn9VT4qUv7Ef0iZe+xEs/gNjvy7nG2LDNUAaAgoICFBQU1Htv5szaxzJ79OiB7du3n/a4/v37Y968eeEMjZqB5AQTpozqiLH5rfHxjwfw3fpCfL/xKIb2ysb4i9ogJTE6EstEREShlpVqw7QJ3fD8hxvx38924LbxXaI+SUeRoaS2gvXyP8P1+Qtwf/EStH5HYex7mS4/H4HivQiUHoKx8zCY+kyI+PmJKDiaswySLUXvMIiISAfRsdw2URilJJpx46hO+NsdF2FIzyx8t6EQD766HLOX7ERZVcPH9YiIiOJB7w5puHxwWyzbfAxfrDmsdzgURWRrEqwT/gi1wyB418yDVrwn4jH4dv+ImkV/hXf9JxB+b8TPT0TBE86yqKvHTkREkRHWGcpE0SQ1yYypYzpj3EVt8NGyA/hm/RF8v7EQ/zO5Nzq0dOgdHhERUchNGJSLA0VVeO/L3WiVbkfnNvziT7UkxQDzxbdD6zocSov2EEIA3hpIJltYzyuEBu+a+fCuXQglsyPMI38NSTWG9ZxEFB5qbt+4XtSUiIjOjDOUqdlJS7Lg5rGd8dSv8uGwm/DKgi2odvn0DouIiCjkZEnC7RO6okWKBS/N34wTFW69Q6IoIkkSlBbtAQC+rV/C+f6fECjZF7bzCZ8H7i9egnftQqgdh8Ay/g+QT7O4IxHFBtMFV8LQaYjeYRARkQ6YUKZmK91hwZ2Xd0NVjRevf7QVWvjWpyQiItKNxaTi11f0QEDT8OLcTfD6AnqHRFFIyewEyApqFj4F356VYTmHcFcicGwnTPnXwjzsVkgKH5YkilXC60Kg9BBL1hARNVNMKFOzlpuZiGtHdMDGPSewZOUhvcMhIiIKi6xUG6YVdMPBoiq8uXh7bXkDop9RUlvBOmk6lLRcuL98CZ7V8yCEFpK2A8f3Q/O6ICekw3bt/8HYcywXiSSKcYHivaj54H/D+lQDERFFLyaUqdkb0TcH/Tqm44Nv9mD3kQq9wyEiIgqL3u3TMHFIWyzfUoTPV3ORPmpItiTCMuEPUDsOgnftAnhXfhB0m77dy1Gz4AmUfjULACAZLUG3SUT6E85SAOCifEREzRQTytTsSZKEW8Z1RkqiCa8s2Mx6ykREFLcmDMxFnw5pmPPVbmw7UKZ3OBSFJMUA87DbYbroehi6XNzkdoTQ4Fn1IdxfvQolIw/JQ68NXZBEpDvNWTuGSFaHvoEQEZEumFAmAmA1G3DX5d1RUe3Fvz/exkeBiYgoLv18kb6X52/G8QqX3iFRFJIkCcYeoyAnZkD43Kj59J8IFO9t9PHC54H78xnwrlsEQ6ehsIx7AIqVi+8RxRPhLIVkToCkGvUOhYiIdMCEMtFJbbMScc2I9li/+ziWrGI9ZSIiik8Wk4rfXNkTAU3gxbmb4OEifXQWoqYCWnkhahb9Fb7dPzbqGO/GxfAfWAvTRdfBNPQWLr5HFIc0ZxkkW4reYRARkU6YUCb6mUv7tUTfk/WU9xSynvK5aEIwEUFEFIMyU6z4VUFXHCqq5iJ9dFZyUgtYL/8zlPS2cH/1Cjyr555xsT7hcwMAjL3HwTLhQRh7jObie0RxSrYkQcloq3cYRESkEyaUiX7mVD3l5AQTXpm/BU436ymfzezPd+KGP3+KL9cchsZkBBFRTOnVPg2XD2mLH7cU4XM+mUNnIVsSYRn/ANSOQ+BduxDuL16CCPjr7ePbtQzOdx5AoPQwJMUANauTTtESUSSYh90K85Cb9Q6DiIh0woQy0S/YzAbcObE7yqs9rKd8FvuOVuKbtUdgNRsw+/Od+L/Za3H0hFPvsIiI6DyMH5iLvh3TMefrPdi2v1TvcCiK1S7WdytM+ddCMloBWQFwcvG9lR/A/fVrkJOzIVmTdI6UiMJNCHHGJxWIiKh5YEEzotNol52Iqy/Ow7tf7cbnqw9j1AWt9A4pqmhC4O3PdyLBZsRLfxiBJcv24d0vd2H6v1fh8iFtMfrCVlBk3q8iIop2siThtvFd8ORba/Dygi1wJFvhrvGGrP10hwV2iyFk7ZG+JEmCsedYCCEgSRL8hzbCu/kLBA5thKHzMJgG3ch6yUTNgKgshnPOwzBfcgcM7S7UOxwiItIBf+MjOoORF7TC9oPleP/r3ejQMglts7g6+SnLNx/DnsJK3Da+C2wWAwb3zEKPdimYtWQnPvhmD1ZtK8Yt4zqjdYsEvUMlIqJzsJhU/OaKHnj8zdX408vLQtq2zazij9f3RcsMe0jbJX1JklQ7M3nF+9DKDsN00XUwdB/FeslEzYTmLAVEAJKJn+1ERM0VE8pEZyBJEm4d3wWP/WclXp6/GY/ecgGsZs6ycnn8eP+bPcjLTsRF3TPr3k+ym3DPFT2wensxZi3Zgb+8uRpj89ugYGAuDCpnKxMRRbMWKVY8fuuFqPQGUFHhCkmbfr+Gd77chWfeXYc/TumLrFRbSNql6CBJMqwFD0KrKYeSnKN3OEQUQcJZBgCQbSk6R0JERHphQpnoLOyW2nrKf5u9Fv/+ZDvumdS92c++WbR0P6qcXvz2qp6QT/N30b9zBjq3Sca7X+7CR8v2Y82OYtwyrgva57CmIhFRNEtNMqNzegJKSqpC1mZOug3/N3stnnl3Pf44pS8yHJaQtU36k0w2KCbeKCBqbjRnbc19yZascyRERKQXThskOoe8nCRcOSwPa3eW4Ms1hyN23gqnF3O+2o3N+05E7JzncvSEE5+vPoTBPbPOWgLEbjHg9gld8bure8HjC+Cvb63B21/shMcbiGC0RESkt6xUG+6f3AdeXwDPvLMOpZVuvUMiIqIgieoywGiFZDDpHQoREemECWWiRhh9YSv0ykvFe1/txr6jlWE9lz+gYfGKg3j4teVYvPIgXp6/GSXloXn8OBhCCLz9xS4YDQquHJbXqGN65qXiL7cNwMV9c/DF6sP433+twNb9pWGOlIiIoknLDDv+Z3JvON0+/P2ddaio9ugdEhERBUG4q1jugoiomWNCmagRJEnCbRO6IsluxCsLNqPG7Q/5OYQQWL/7OP739RWY8/VudGjpwO+v7QUAeG3RFgQ0LeTnPB/rdx3Hln2luHxwWyTajI0+zmJSceOoTvjj9X2gyBKeeXc93vh0G2rcvjBGS0RE0SQ3MxH3Xd0b5dVePPPuelTVePUOiYiImshy6d2wTvqz3mEQEZGOmFAmaiS7xYA7L+uOExUevPHpNgghQtZ24XEnnp2zAc9/sBGSJOF3V/fE767uhe5tU3Hj6E7Yc6QSC37YH7LznS+fP4B3vtyFnDQbhvdt2sI7nVon47FbL8TYAa3x/cajeOT1FVi3qyTEkRIRUbRq3zIJ917VE8XlLvzjvfW8sUhEFMMktfETTIiIKP4woUx0Htq3TMKVF7fD6h0l+GrtkaDbc7p9eOeLXZj+75XYU1iJySPa4/HbLkTPvLS6ffK7ZmJQj0x8vGw/th8oC/qcTbF4xUEcr3Dj+ks7QFWa/rFhNCi4enh7PDK1P+wWA174cBNeWbAZlZypRkTULHRpk4xfX9EDR0pqb6S6PKF/4oeIiMJHBPxwzn0Uvt0/6h0KERHpiAllovM0+sLW6JmXive+2oUDx6qa1IamCXy6fD8eevVHfHFykbu//iofoy5sfdqE7ZSRHZGRbMHMj7ai2hXZGV2llW58vPwA+ndKR5fc0NRKa5uViD/ffAEuH9IWa3aU4JGZK/Dj1mMhnfVNRETRqUe7VNx1eXfsO1qF5z/YCI+PC7YSEcUKUVMG7fh+wM8JIUREzRkTykTnSZYk3Da+CxKsRrw8f/N5z67afqAMj72xCi99sAHZaTb8+eYLcNOYzmetS2w2qrhjYjdUOr34zyehLbdxLu99tRsAcM2I9iFtV1VkXDaoLR695QJkJFvw2sKteP6DjSir4mJNRETxrm/HdEwr6Iqdh8rx4txN8Pn1XSeAiIgaR3PWPjEp2bkoHxFRc8aEMlETJFiNuHNiNxyvcOONT7c3KsF7vNyFl+ZtwtPvrEON24c/Tu2PP17fB20yExp1ztzMRFw5LA/rdh3HN+sLg+1Co2w/UIZV24sxLr8N0pIsYTlHTrodD9/QD5NHtMe2A2V45PUVTZ75TUREsWNA1xa4eVxnbNlXipfnb4Y/wKQyEVG0E9WlAADJlqxzJEREpCcmlImaqENLByYNbYtV24vPmuD1eAOY+91ePDxzBTbuOYHLh7TFk9PyMbhXDiRJOq9zjrqwFbq1TcG7X+7CkZLqYLtwVgFNw9tf7ERakhljBrQO67lkWcKoC1vjsdsuhMWk4Ln3N+B4hSus5yQiIv0N6ZmNG0Z1xPrdxzFz0VZoGksfERFFM3FyhrJs4wxlIqLmjAlloiCMzW+D7u1S8M4Xu3CwqP6sWiEElm85hodn/oiPlu1H/07peOpX+bhsUFsYDUqTzidLEm4f3wUWo4JXFm6BN4x1J79ZV4jDJU5cO6JDk+M9Xy2Srbjv6l7w+TU8O2dDxOtFExFR5I3o2xLXDG+PVduL8Z9PtkFjPX0ioqilOcsAgxmSMTxPLxIRUWxgQpkoCLIk4fYJXWG3qPXqKe87WomnZq3BzEVbkWgz4qEb+uJXl3VDSqI56HMm2U24dXxXHClxYs7Xu4Nu73Qqa7yY991edM1NRt+OaWE5x5nkpNvxmyt7oKTchRc/3Aifn4s1ERHFuzEDWuPyIW2xdPMxzFqyk4u0EhFFKWPvcbCO/4PeYRARkc5UvQMginWJViPunNgd//f2Wvz7422wmFT8sOkoEq0G3DK2Mwb1zIJ8nqUtzqVnXipGXdAKS1YdQre2KejTIT2k7c/7bi88vgCuu7TjeZflCIVOrZNx+4SueGXBFsz8aBvunNgt5H+HREQUXQoG5sLr0/DJjwdgVGVcO6K9LmMQERGdmWx1AFaH3mEQEZHOmFAmCoGOrRyYNKQd5n63F4osYcyA1igYmAuLKXz/xK4cloftB8vwn0+2I/fWRCQnmELS7v5jlfhufSFGXtAKOWm2kLTZFBd2aYHSSg/mfL0bcxJMmHxJB91iISKi8JMkCVcOawevP4Alqw7BaJBxxdA8vcMiIqKfcf/4LpTMDjDk9tM7FCIi0hETykQhMu6iNkiyGdGxlQMtUqxhP59BlXHHZd3w2BurMHPRFtw/uQ9kObiZXEIIzP58JxKsBlw2qG2IIm260Re2QmmlG0tWHUJKohmjLmild0hERBRGkiThuks6wOfX8NGyAzCoCgoG5ob8PF5fAAeLqlHh9KBX+zSoCqvAERGdi9AC8G36DJJiAJhQJiJq1phQJgoRWZIwpFd2RM+ZlWrDlEs74j+fbsenKw5g/EW5QbW3fMsx7DlSiVvGdYbVrP/HgyRJmHxJB5RVefDel7uQkmBC/84ZeodFRERhJEkSbhzdCV6fhnnf7YVJlTHqwtZNbi+gaThS4sT+Y1XYW1iJ/UcrcbjEWbf4X6dWDtxzRQ/YLYZQdYGIKC6JmgpACEi2FL1DISIinemfMSKioAzumYXN+0ox77t96NwmGXnZSU1qx+Xx4/2v96BtViIG9cgKcZRNJ8sSphV0xTPvrsdrJxc57NjKoXdYREQURrIk4dbxneHzB/DuV7thMCgY3ifnnMcJIVBc5sKWQxXYuKMY+45W4mBRFbx+DQBgNalom5WAsfmt0TYrEdUuH2Yt2Ym/vLkK917VS9dST0RE0U44SwEAsj1Z50iIiEhvTCgTxThJknDTmE7YW1iJVxdswaO3XNik2cWLlu1HhdOL31zZM+oWwDMaFNx7VU889dYavPDhRjx0Qz9k80s/EVFcU2QZv7qsG3xzN+Gtz3bAqMoNbniWVXmw/2gl9h6tnXm8/1gVnG4/AMCoymidmYBhvXPQNisBbbMTkeGwNFjoLyfdhhc/3ISn3lqNOy7rjp55qRHrIxFRLNGcZQDAGcpERMSEMlE8sJoNuOOybvjb7LWYtWQHphV0bfCF+WyOnnDi81WHMLhHFtplJ4Yx0qazWwy475peePKtNXh2zgb8aWo/OOyhWYiQiIiik6rIuHtSd/y/Dzbi359sQ1WND/6Ahn1HK7HvaCXKq70Aamc0t0y3oX/nDLTNSkTfrpmwKLVJ6XPJy07C/97UH89/sBH/74MNuHZ4e4y8oNV5jaNERM1B3QxlJpSJiJo9JpSJ4kT7lkm4bHAu5n+/D93apjS6bIUQAu98uQtGg4wrL84Lc5TBSXdY8Lure+L/Zq/Dc+9vwB+v7wuLKbY/xvYWVuJISTX8mkAgoMEfEAhoGgIBUfdeQBPw//z/p9sWEPCfPE5RZWgBDbIsQZYkKLJU9+efvyfJJ7dJEmQZP/vzz/5/8s965VWsViNqarwRPackAYN6ZKFFcvgX1ySiczOoCn5zRU88O2c95ny9GwDQIsWKzm2S0TYrEW2zEtE6ww6jQak7Jj09ASUlVY0+R0qiGQ/d0A+vf7QV7361G0eOO3Hj6E5crI+I6GeU7K4wDZwCmPikIBFRcxfbmRgiqmfCRbnYur8Msz7fifYtkxqVENuw+wQ27y3F5BHtkWQzRiDK4ORmJuKuy7vj+Q824uX5m3HvVT1j8gv/8QoX5ny9B6u3F59xHwmAoshQldrEr6rIUBQJqlz7f0U+ue3keyaDAtUsw2hU4fb4IDSBgCagCcDv007+WUD7+f/r9vnptSbQ4D29SBIgInx6SZKQlWJjQpkoipiMCv5ncm8cLK5GVooVVnPoF9AzGRXcNak75n+/Dx8t24+iMhfumdQdCdboHxuJiCJBSW0FJbWV3mEQEVEUYEKZKI7IsoRfFXTF9H+vxKsLtuDhG/udNdnq8wfwzpc7kZVqxYh+LSMYaXB65qVi6phOeOPT7fjv4h24ZVznmHk02eMLYPGKg/jkxwOQAEwc3BYDu2fCoMp1SWP1ZLJYlpvWp/OdmRfN4qkvRBQcg6o0eeHZxpIlCVcMbYfsNCv+/fF2/OXN1fjtVT2Rk24P63mJiGKBb+8qSFYH1MwOeodCREQ6i71pfUR0VimJZtw8tjP2H6vC3O/2nnXfz1YeQkm5G9eP7Bhzs3yH9srGZYNy8cOmo1jwwz69wzknIQRWbS/GIzN/xIIf9qF3+zQ8OS0fEwe3RbrDAofdhASrERaTCoOqNDmZTEREwcvvmokHp/SFz6/hybfWYMPu43qHRESkO8+K9+Db+pXeYRARURSIrQwSETVKv04ZuLh3NhavOIgt+0pPu09ppRsfLd+Pfh3T0S03NhfWmDi4LQb3yMLCpfvx3YZCvcM5o0PF1fj7O+vw8vzNsJoN+OP1fXDX5d2RmmTWOzQiIjqDdtmJ+N+b+qNFshXPf7ARi1cchIh0DR4ioighhAbhLINsj83vDUREFFpMKBPFqWsv6YDsNBte/2grKk+zqNmcr3dDCODaEe11iC40JEnC1DGd0L1tCv67eAc27jmhd0j1VLt8eGvJDjz6n5U4VFyNG0d3wvSbL0Cn1sl6h0ZERI2QkmjGg1P6ol+ndMz5ejf+88l2+AOa3mEREUWccFUBWgCSjb/HEhERE8pEcctkUHDHZd3gdPvx74+31ZtVteNgGVZuK8bYAa2R5rDoGGXwVEXGXZd3R8sMG16evxn7j1XqHRICmoYv1xzGQ68ux7frCjGiT0v89Y6LMLxPDktZEBHFGJNRwZ2Xd68rs/TMO+tOe6OWiCieCWcZAECycYYyERExoUwU11pl2HHtiPbYuOcEvlh9GEBtsnP257uQmmjC2Pw2OkcYGhaTit9d3Qt2iwHPvb8RJeUu3WLZdqAMj/1nFWZ/vhOtWyTg0VsvwJRRHWG3GHSLiYiIgiNLEi4f0g53TuyGfceq8MSbq3G4pFrvsIiIIkZz1pbRkzlDmYiIAKh6B0BE4TWibw627CvF+9/sRqfWDuw6XIHDJdW4+/LuMBkUvcMLGYfdhPuu6YW/zlqDZ+dswMM39otoEvd4hQtzvtqN1TtKkJZkxj2TuqNvx3RIEmckExHFiwu7tEC6w4LnP9yIJ99agzsu64be7dNC1r4QAiUVbuwrrMS+o5U4cKwKvoCA3x8I2TkSbEbkZiagXVYicrMSkZxgClnb8UgTAkWlNdh/tAp7j1biQFEVNA0hvSZ6UVVFl37YrQbcfXkPWM38KhpLZGsS1I5DICWE7jOPiIhiV1hH8UWLFuHll1+G3+/HTTfdhClTptTbvm3bNvzpT3+C0+lE//798dhjj0FVVRQWFuKBBx7AiRMn0LZtWzzzzDOw2WzhDJUobkmShFvGdcaf/70SryzYgqoaL7q0SUa/Tul6hxZy2Wk2/ObKnnjm3fV4/oONuH9ybxjDnDT3+AL49McD+HTFQUgSMGlIW4y+sHXYz0t0Jk0de4mocdpmJeLPN12A5z/ciBc+2Iirh7fH6AtbNekGYkW1B/tOJir3H63E/mNVqHb5ANSWdGrTwo70ZCs8Hn/I4i+tdOPTHw9CO1kKy2E3om1WYt1/uVkJsJmb51M1QgiUVXmw72gl9h2twr6T18R18u/fZFDQuoUd6cmWkF4TvZhMqi79sFsNUFgCLOYoGXmwZOTpHQYREUWJsH2DLCoqwrPPPou5c+fCaDRi8uTJGDBgANq3/2kBsAceeABPPPEEevfujYcffhhz5szB9ddfj8ceewzXX389xo8fjxkzZuCll17CAw88EK5QieJegtWIaRO64h/vrockSbj+0g5xO3O2YysHflXQFS/P34zXFm3F3Zd3D0vdYiEEVm0vxpyvd6O00oMLu2TgmuHtkZJoDvm5iBormLGXiBovOcGEB6f0xb8+3oY5X+/GkePVmDq6MwzqmavJ1bj92H+sdubxqdmuZVUeALUlNbLTbOjTIQ1tsxPRNjMROek2qIqM9PQElJRUhTR+jy+AQ0XVtYnTY7XJ03W7jtdtb5FsqZdkbt3CHpc3SqtdPuw/WnkyoV+bQK5w1tbHVmQJLTPsGNC1BdpmJaBtViKyU22QZSks10QP8dIPigyt4higmljygoiIAIQxobxs2TLk5+fD4XAAAEaPHo3Fixfj17/+NQDgyJEjcLvd6N27NwDgiiuuwPPPP4+rr74aq1atwowZM+rev+GGG5hQJgpS19wU3Dy2MwSAnHS73uGEVf/OGZh8SQe88+UuvPPFLozNbw1VlaHKMgyqBEWRIQeRUD9YVIW3v9iFnYfK0TrDjl8VdEPHVo7QdYCoiZo69jKhTHT+TAYFd07shoWpVixcuh9FZS78elIPJNqM8PkDOFhUXTfzeN/RKhwrrak7NiPZgo6tHGibmYC22Ylo3SIhomWoTAYF7VsmoX3LpLr3atw+7DtWVRfvjkPl+HFrEYDahHfLdBtysxLrkqs56TYocuwsx+LxBnCgqOrk7OPa/0rK3QAACUBmqhXd2qbUzdJunWGHQY2/JDpRU7m/ewMQGqyXPax3KEREFAXCllAuLi5GevpPj9RnZGRg48aNZ9yenp6OoqIilJWVwW631z1+e+p9IgrekF7ZeocQMSMvaIUTlW4sWXUIX6493GC7IktQFRmqcur/MkxGBZKEuvcNigxFkWH42X5ev4Z1u0pgMxswdUwnDO2ZHZYZ0ERN0dSxl4ia5tRifdlpNvzr42147I1VSLQacbikGgGttqREkt2IdlmJuKh7JtpmJSA3MzEqF2q1mg3olpuCbrkpde+VV9cv/7BmRzG+21AIADCqMlq3SEBmihWNySubzUa43d5whX9GPr+GQ8XVOHLciZNVPpCaaELbrERc3DsHuVmJyM1MgMXE0j8UXwLFe1CycnmDf3dyejsYu1wM4XHCs+K9hgcarTDnTwYAeFZ9COGqqG2v9BDUnG5hj5uIiGJD2H5z0jSt3iP1Qoh6r8+0/Zf7ATjvR/NTU0M3+zI9PSFkbektXvoSL/0A4qcv0dqPe67pgwu6Z6Gi2gOfX4M/oNX+36/B18g/+/0aXB5f3Z8DmoYJg9vh+lGdYLca9e7iGUXrNWmKeOpLuDV17D1fHGcbipd+APHTl0j2Y3x6Ajq2TcUrczfCajLgwu6Z6NAqGR1bO5CaZAm6fb2uSXp6Ajq0/WkBLiEEjp2owa5DZdh5sBy7DpVh64EyXWJrLEWR0LpFAgb3bomOrR3o0CoZjhAsRMh/JxTtNGc5PLvXQtO0eu+rcm0KQAR88B/c2OA4yfLTkwuBwu3Qqkpq31eNUFv3DGPEREQUS8KWUM7MzMTq1avrXpeUlCAjI6Pe9pKSkrrXx48fR0ZGBlJSUlBVVYVAIABFURoc1xgnTlRDOzkrJBjxVFcsXvoSL/0A4qcv0d6PvBZ2oEXjkl/n0xeX0wOX0xNMaGET7dfkfMR6X2RZCmny9VyaOvaeL46z9cVLP4D46Yse/XCYVTx4fd9672lef9BxRNs1UQF0aZmELi2TALRp9HHR0g+f24uSIGdKR0tfghUP/Yj0OBtLDG37IfvCi894jWWrA/YbnjtrG9aJfwpDZEREFA/CVvhs4MCBWL58OUpLS+FyubBkyRIMHTq0bntOTg5MJhPWrFkDAFiwYAGGDh0Kg8GA/v3745NPPgEAzJ8/v95xREREdHpNHXuJiIiIiIiIGitsCeUWLVrgvvvuw9SpU3H55ZdjwoQJ6NmzJ6ZNm4ZNmzYBAJ555hn89a9/xZgxY1BTU4OpU6cCAKZPn445c+Zg3LhxWL16NX73u9+FK0wiIqK4EczYS0RERERERNQYYV19oqCgAAUFBfXemzlzZt2fO3fujA8++KDBcTk5OXjrrbfCGRoREVFcaurYS0RERERERNQYYZuhTERERERERERERETxhQllIiIiIiIiIiIiImoUJpSJiIiIiIiIiIiIqFGYUCYiIiIiIiIiIiKiRmFCmYiIiIiIiIiIiIgahQllIiIiIiIiIiIiImoUJpSJiIiIiIiIiIiIqFGYUCYiIiIiIiIiIiKiRmFCmYiIiIiIiKgRFi1ahHHjxmHUqFGYPXt2g+0vvvgihg8fjokTJ2LixImn3YeIiCjWqXoHQERERERERBTtioqK8Oyzz2Lu3LkwGo2YPHkyBgwYgPbt29fts3nzZvzzn/9Enz59dIyUiIgovDhDmYiIiIiIiOgcli1bhvz8fDgcDlitVowePRqLFy+ut8/mzZvx6quvoqCgAI8//jg8Ho9O0RIREYUPE8pERERERERE51BcXIz09PS61xkZGSgqKqp77XQ60aVLFzzwwAOYN28eKisr8dJLL+kRKhERUVix5AURERERERHROWiaBkmS6l4LIeq9ttlsmDlzZt3rW2+9FQ8//DDuu+++Rp8jNdUemmBPSk9PCGl7eomXfgDx0xf2I/rES1/ipR9AfPXll5hQJiIiIiIiIjqHzMxMrF69uu51SUkJMjIy6l4XFhZi2bJluOqqqwDUJpxV9fy+cp84UQ1NEyGJNz09ASUlVSFpS0/x0g8gfvrCfkSfeOlLvPQDiP2+yLJ01puccZlQlmXp3Dvp0Jbe4qUv8dIPIH76Ei/9AOKnL/HSDyC2+xLLsZ8Nx9mG4qUfQPz0JV76AcRPX+KlH0D89CXW+xHp+AcOHIgXXngBpaWlsFgsWLJkCf7yl7/UbTebzfj73/+OAQMGoGXLlpg9ezZGjhx5XucIdZ9i/RqfEi/9AOKnL+xH9ImXvsRLP4DY7su5YpeEEKG5/UlEREREREQUxxYtWoRXX30VPp8PV111FaZNm4Zp06bh3nvvRY8ePfDZZ5/hhRdegM/nQ9++ffHYY4/BaDTqHTYREVFIMaFMRERERERERERERI0i6x0AEREREREREREREcUGJpSJiIiIiIiIiIiIqFGYUCYiIiIiIiIiIiKiRmFCmYiIiIiIiIiIiIgahQllIiIiIiIiIiIiImoUJpSJiIiIiIiIiIiIqFGYUCYiIiIiIiIiIiKiRmFCmYiIiIiIiIiIiIgahQllAIsWLcK4ceMwatQozJ49u8H2bdu24YorrsDo0aPxpz/9CX6/X4coz+3FF1/E+PHjMX78eDz99NOn3T58+HBMnDgREydOPG1fo8WNN96I8ePH18W6YcOGettj5Zq8//77dX2YOHEi+vXrh8cff7zePtF+XaqrqzFhwgQcPnwYALBs2TIUFBRg1KhRePbZZ097TGFhIaZMmYIxY8bgrrvugtPpjGTIp/XLfrz33nuYMGECCgoK8NBDD8Hr9TY4Zt68eRg8eHDdtTlTfyPtl3156KGHMGrUqLo4P//88wbHRPs1+fbbb+v9W8nPz8cdd9zR4JhovSZ0dhxno088jLMcY6Pn8xyIn3E2XsZYgONscxEvYywQP+NsPIyxAMfZaPpMj5cxFoifcZZjLADRzB07dkwMHz5clJWVCafTKQoKCsSuXbvq7TN+/Hixbt06IYQQDz30kJg9e7YOkZ7d0qVLxbXXXis8Ho/wer1i6tSpYsmSJfX2ueOOO8TatWt1irDxNE0TgwcPFj6f74z7xMI1+aWdO3eKkSNHihMnTtR7P5qvy/r168WECRNEt27dxKFDh4TL5RLDhg0TBw8eFD6fT9x6663im2++aXDcr371K/HRRx8JIYR48cUXxdNPPx3p0Ov5ZT/27t0rRo4cKaqqqoSmaeIPf/iD+M9//tPguMcff1wsWrQo8gGfxS/7IoQQEyZMEEVFRWc9Ltqvyc8VFxeLSy65ROzbt6/BcdF4TejsOM5Gn3gcZznG6itextl4GWOF4DjbXMTLGCtE/Iyz8TjGCsFxVk/xMsYKET/jLMfYWs1+hvKyZcuQn58Ph8MBq9WK0aNHY/HixXXbjxw5Arfbjd69ewMArrjiinrbo0V6ejoefPBBGI1GGAwG5OXlobCwsN4+mzdvxquvvoqCggI8/vjj8Hg8OkV7dnv37gUA3Hrrrbjsssswa9asettj5Zr80qOPPor77rsPKSkp9d6P5usyZ84cTJ8+HRkZGQCAjRs3ok2bNmjVqhVUVUVBQUGDv3ufz4dVq1Zh9OjRAKLj+vyyH0ajEdOnT4fdbockSejYsWODfy8AsGnTJsybNw8FBQW4//77UVFREenQG/hlX1wuFwoLC/Hwww+joKAAzz//PDRNq3dMLFyTn3v66acxefJk5ObmNtgWjdeEzo7jbPSJx3GWY6y+4mWcjZcxFuA421zEyxgLxM84G49jLMBxVk/xMsYC8TPOcoyt1ewTysXFxUhPT697nZGRgaKiojNuT09Pr7c9WnTo0KFuUNq/fz8+/fRTDBs2rG670+lEly5d8MADD2DevHmorKzESy+9pFO0Z1dZWYmLLroIM2bMwBtvvIF3330XS5curdseK9fk55YtWwa3242xY8fWez/ar8uTTz6J/v37170+178XACgrK4PdboeqqgCi4/r8sh85OTkYNGgQAKC0tBSzZ8/GJZdc0uC49PR03H333Vi4cCGysrIaPOKlh1/25fjx48jPz8dTTz2FOXPmYPXq1fjggw/qHRML1+SU/fv3Y+XKlZg6deppj4vGa0Jnx3E2+sTbOMsxVv9rEy/jbLyMsQDH2eYiXsZYIH7G2XgbYwGOs3pfn3gZY4H4GWc5xtZq9gllTdMgSVLdayFEvdfn2h5tdu3ahVtvvRV/+MMf6t0RsdlsmDlzJvLy8qCqKm699VZ8++23+gV6Fn369MHTTz+NhIQEpKSk4KqrrqoXa6xdEwB49913ccsttzR4P5auC9C4v/vTvRet16eoqAg33XQTrrzySgwYMKDB9hkzZqBfv36QJAm33347vv/+ex2iPLtWrVphxowZyMjIgMViwY033tjgZyiWrsl7772H66+/Hkaj8bTbY+GaUH0cZ6NPvI2zHGOj99rE+jgbb2MswHE23sTbGAvE/jgbb2MswHE2Wq9PrI+xQPyNs81tjG32CeXMzEyUlJTUvS4pKak3bf2X248fP37aae3RYM2aNbj55pvxP//zP5g0aVK9bYWFhfXu9Agh6u7wRJvVq1dj+fLlda9/GWssXRMA8Hq9WLVqFUaMGNFgWyxdF+Dc/14AICUlBVVVVQgEAmfcJxrs2bMHkydPxqRJk3DPPfc02F5VVYU33nij7rUQAoqiRDDCxtmxYwc+++yzuten+xmKlWsCAF9++SXGjRt32m2xck2oPo6z0SeexlmOsdH7eR4P42y8jbEAx9l4E09jLBAf42w8jbEAx9lo/UyPhzEWiL9xtrmNsc0+oTxw4EAsX74cpaWlcLlcWLJkCYYOHVq3PScnByaTCWvWrAEALFiwoN72aHH06FHcc889eOaZZzB+/PgG281mM/7+97/j0KFDEEJg9uzZGDlypA6RnltVVRWefvppeDweVFdXY968efVijZVrcsqOHTuQm5sLq9XaYFssXRcA6NWrF/bt24cDBw4gEAjgo48+avB3bzAY0L9/f3zyyScAgPnz50fd9amursZtt92G3/72t7j11ltPu4/VasXrr79etyrzrFmzovLaCCHw1FNPoaKiAj6fD++9916DOGPhmgC1j2y53W60atXqtNtj5ZpQfRxno088jbMcY6Pz8zxextl4GmMBjrPxKF7GWCB+xtl4GmMBjrPR+JkeL2MsEF/jbLMcY8Oy1F+MWbhwoRg/frwYNWqUeO2114QQQtx+++1i48aNQgghtm3bJq688koxevRo8fvf/154PB49wz2tv/zlL6J3797isssuq/vv7bffrtePxYsX1/XzwQcfjMp+nPLss8+KMWPGiFGjRok33nhDCBF71+SUjz/+WPzud7+r916sXZfhw4fXrV66bNkyUVBQIEaNGiWefPJJoWmaEEKIhx9+WHzxxRdCCCEOHz4sbrjhBjF27Fhx6623ivLyct1i/7lT/fjPf/4junXrVu/fy3PPPSeEqN+PVatWicsvv1yMGTNG3HnnnaKyslLP8Ov5+TWZNWuWGDt2rBg5cqT4+9//XrdPLF0TIYTYsGGDuPrqqxvsEyvXhM6M42z0iZdxlmNs9HyeCxE/42y8jLFCcJxtDuJhjBUivsbZeBljheA4G02f6fEyxgoRP+Nscx9jJSGE0DupTURERERERERERETRr9mXvCAiIiIiIiIiIiKixmFCmYiIiIiIiIiIiIgahQllIiIiIiIiIiIiImoUJpSJiIiIiIiIiIiIqFGYUCYiIiIiIiIiIiKiRlH1DoCIwqNTp07o2LEjZLn+faMZM2agZcuWIT/X8uXLkZKSEtJ2iYiIohXHWSIiovDgGEsU/ZhQJopjb775JgdGIiKiMOE4S0REFB4cY4miGxPKRM3QihUr8MwzzyA7Oxt79+6F2WzG3/72N+Tl5aGqqgqPPfYYtm/fDkmSMGTIEPz+97+HqqrYsGEDnnjiCbhcLhgMBvzhD3/ARRddBAB44YUXsGHDBpSXl+O2227DlClTdO4lERGRPjjOEhERhQfHWKLowIQyURy76aab6j0m1LJlS8yYMQMAsHnzZvzxj39E//798c477+CBBx7A3Llz8cQTT8DhcGDRokXw+Xy466678O9//xu33HIL7rnnHjzxxBO4+OKLsXnzZjz00ENYsGABAKBVq1aYPn06tm7dimuvvRbXXHMNDAaDLv0mIiKKBI6zRERE4cExlii6MaFMFMfO9phQ586d0b9/fwDAlVdeiccffxxlZWX47rvv8M4770CSJBiNRkyePBlvvvkmBg0aBFmWcfHFFwMAunfvjkWLFtW1N2HCBABAly5d4PV6UV1djeTk5PB2kIiISEccZ4mIiMKDYyxRdJPPvQsRxSNFUU77nqZpkCSp7j1N0+D3+6EoSr33AWDnzp3w+/0AAFWtvT91ah8hRLhCJyIiinocZ4mIiMKDYyyR/phQJmqmtm/fju3btwMA3nvvPfTp0weJiYkYPHgwZs2aBSEEvF4v5syZg4EDB6Jdu3aQJAlLly4FAGzZsgU33XQTNE3TsxtERERRieMsERFReHCMJdIfS14QxbFf1p0CgN///vcwm81IS0vDc889hyNHjiAlJQVPP/00AOCRRx7BE088gYKCAvh8PgwZMgR33nknjEYjXnjhBTz11FN4+umnYTAY8MILL8BoNOrRNSIiIt1xnCUiIgoPjrFE0U0SnMtP1OysWLECf/nLX/DRRx/pHQoREVHc4ThLREQUHhxjiaIDS14QERERERERERERUaNwhjIRERERERERERERNQpnKBMRERERERERERFRozChTERERERERERERESNwoQyERERERERERERETUKJumPCwAAADFJREFUE8pERERERERERERE1ChMKBMRERERERERERFRozChTERERERERERERESN8v8BiswBcF0kJtkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x504 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_performance(loss=losses, error=errors, accuracy=accuracies, log_file=f'{log_dir}/attention_base.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>GatedAttention-based Network</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "set_seed()\n",
    "network = GatedAttention().to(device=device)\n",
    "optimiser = AdamW(params=network.parameters(), lr=5e-4, weight_decay=1e-4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 style=\"color:rgb(0,120,170)\">Operate GatedAttention-based Network</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "losses, errors, accuracies = operate(\n",
    "    network=network,\n",
    "    optimiser=optimiser,\n",
    "    data_loader_train=data_loader_train,\n",
    "    data_loader_eval=data_loader_eval,\n",
    "    num_epochs=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x504 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_performance(loss=losses, error=errors, accuracy=accuracies, log_file=f'{log_dir}/gated_attention_base.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Hopfield-based Pooling</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "class HfPooling(Module):    \n",
    "    def __init__(self):\n",
    "        \"\"\"\n",
    "        Initialize a new instance of a Hopfield-based pooling network.\n",
    "        \n",
    "        Note: all hyperparameters of the network are fixed for demonstration purposes.\n",
    "        Morevover, most of the notation of the original implementation is kept in order\n",
    "        to be easier comparable (partially ignoring PEP8).\n",
    "        \"\"\"\n",
    "        super(HfPooling, self).__init__()\n",
    "        self.L = 500\n",
    "        self.D = 128\n",
    "        self.K = 1\n",
    "\n",
    "        self.feature_extractor_part1 = Sequential(\n",
    "            Conv2d(1, 20, kernel_size=5),\n",
    "            ReLU(),\n",
    "            MaxPool2d(2, stride=2),\n",
    "            Conv2d(20, 50, kernel_size=5),\n",
    "            ReLU(),\n",
    "            MaxPool2d(2, stride=2)\n",
    "        )\n",
    "        self.feature_extractor_part2 = Sequential(\n",
    "            Linear(50 * 4 * 4, self.L),\n",
    "            ReLU(),\n",
    "        )\n",
    "        self.hopfield_pooling = HopfieldPooling(\n",
    "            input_size=self.L, hidden_size=32, output_size=self.L, num_heads=1\n",
    "        )\n",
    "        self.dp = Dropout(\n",
    "            p=0.1\n",
    "        )\n",
    "        self.classifier = Sequential(\n",
    "            Linear(self.L * self.K, 1),\n",
    "            Sigmoid()\n",
    "        )\n",
    "        \n",
    "    def forward(self, input: Tensor) -> Tuple[Tensor, Tensor, Optional[Tensor]]:\n",
    "        \"\"\"\n",
    "        Compute result of Hopfield-based pooling network on specified data.\n",
    "        \n",
    "        :param input: data to be processed by the Hopfield-based pooling network\n",
    "        :return: result as computed by the Hopfield-based pooling network\n",
    "        \"\"\"\n",
    "        x = input.squeeze(0)\n",
    "        H = self.feature_extractor_part1(x)\n",
    "        H = H.view(-1, 50 * 4 * 4)\n",
    "        H = self.feature_extractor_part2(H)\n",
    "        \n",
    "        H = H.unsqueeze(0)\n",
    "        H = self.hopfield_pooling(H)\n",
    "        H = H.squeeze(0)\n",
    "        H = self.dp(H)\n",
    "\n",
    "        Y_prob = self.classifier(H)\n",
    "        Y_hat = torch.ge(Y_prob, 0.5).float()\n",
    "\n",
    "        return Y_prob, Y_hat, None\n",
    "\n",
    "    def calculate_classification_error(self, input: Tensor, target: Tensor) -> Tuple[Tensor, Tensor]:\n",
    "        \"\"\"\n",
    "        Compute classification error of current model.\n",
    "        \n",
    "        :param input: data to be processed by the Hopfield-based pooling network\n",
    "        :param target: target to be used to compute the classification error of the current model\n",
    "        :return: classification error as well as predicted class\n",
    "        \"\"\"\n",
    "        Y = target.float()\n",
    "        _, Y_hat, _ = self.forward(input)\n",
    "        error = 1.0 - Y_hat.eq(Y).cpu().float().mean().item()\n",
    "\n",
    "        return error, Y_hat\n",
    "\n",
    "    def calculate_objective(self, input: Tensor, target: Tensor) -> Tuple[Tensor, Optional[Tensor]]:\n",
    "        \"\"\"\n",
    "        Compute objective of the current model.\n",
    "        \n",
    "        :param input: data to be processed by the Hopfield-based pooling network\n",
    "        :param target: target to be used to compute the objective of the current model\n",
    "        :return: objective as well as dummy A (see accompanying paper for more information)\n",
    "        \"\"\"\n",
    "        Y = target.float()\n",
    "        Y_prob, _, A = self.forward(input)\n",
    "        Y_prob = torch.clamp(Y_prob, min=1e-5, max=(1.0 - 1e-5))\n",
    "        neg_log_likelihood = -1.0 * (Y * torch.log(Y_prob) + (1.0 - Y) * torch.log(1.0 - Y_prob))\n",
    "\n",
    "        return neg_log_likelihood, A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "set_seed()\n",
    "network = HfPooling().to(device=device)\n",
    "optimiser = AdamW(params=network.parameters(), lr=5e-4, weight_decay=1e-4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 style=\"color:rgb(0,120,170)\">Operate HopfieldPooling-based Network</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "losses, errors, accuracies = operate(\n",
    "    network=network,\n",
    "    optimiser=optimiser,\n",
    "    data_loader_train=data_loader_train,\n",
    "    data_loader_eval=data_loader_eval,\n",
    "    num_epochs=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Nlyjp+l4EXd+L2nVsSQrh+uvn4MYbGy/DdDqd0Gg0sFis+OSTtdi1awduuWUeAODXv14Amy0F48ZdhMsum4x16z5q1zGJEk24zYWPb/aJEg3Pscmlta/36a/x6TryegN8zYkodrz+II6WOXH45KzjQyX1qKrzAgAEADmZZgwsyAi3meiSZYFOG8ECMp0VC8rnIWh00Nh7IFR+QOkoRETUTsOHX4i///2vuOqqWdDr9fjVr36OqVNnYPLkK/Dgg/fD7/dj8OChAIDNm7/GG2+8g06d7Hj33X8DAEKh6FwuRRRPBL0ZACD7WVAmotbjOTb58DUnJciyjIpaDyRJPv+DW8krAQ5HQ8T2JwoC7OkmiHHa6sDrD8Lh9Ckd45wcniC27SnD4dLGRe5KqhrQ1LknM8WIglwbLhmWh+65KeieY4PJwLKmUvidbwVNdi/4d34EOeiHoFXPioxERMli/PgJOHBgH370o3mQpBBGjRqLK6+cDkEQkJqahgEDBoV7XN122524++47YDDo0bNnb+TmdkZpaYnCIyCKPsGSjqxrf4EGQ67SUYgogfAcm3z4mlOsSZKMl1fvxtd7KpSOcl72NCMuGZaP8YNzYTXplI4DADhe4cL6rSewaVcZfIHE+EDHatKhIDcFF/Sxh2cfp1hYj4sngnx6l36VqK52ReRTK7vdhspKJ4JHtsL7xb9gmvYgNOmdI5Aw9prGkujUMg5APWNRyzgA9YwlHsZRVnYUOTndOryfs12qGa++P25RFJCZaVUwUXRE+jyb6NQyDkA9Y1HLOAD1jEUt4wDiYyyROM8m+jkWUOd59kzn2Pa+3on2Gp/u9DHHw+9cpKhlLLEchyTLeOXDPfhyZxmuHNUVXbNtEdt3SooR9fXeiO3P4w9i064y7D9eB51WxMh+Wbh0eD4KclMidoyz+f5rEgxJ+GZvJT799jj2ncwzql82+ndPj+vF4jIzLEgzapCZaozrnK2R6L/v5zvHcoZyK2i6DYGl2+KE/2EmIiIiOpf6bz5CUEyFNn+A0lGIiIgoycmyjKVr9+HLnWW4enwBrhpfENH9R6Pgd/HQPByrcOHTkzOCv9xZhoLcFFw6PA8j+2VFvb9vTb0Xn20rwefbS1Df4Ic9zYjrL+kVVzOmzyXRi7DJhAXlVhCExtUbZSkIQeS3jIiIiNTJsWEZxC5DWFAmIiIiRcmyjLfXH8CnW0/gytFdMWNcd6UjtVqXLCvmTumL2RN7YtPuMqz/9jj+/sEevL3+AMYPzsUlw/JgTzNF7HiyLGP7vkq8++l+bNtfBVmWMbhnJi69IB8DCjLitqczJTZWR1vJ981KBL77FJZb/siZykRERKRKotHMRfmIiIhIcSs2HMbazcdw2QX5mD2xZ0LWYcxGLS67IB+XDs9D0VEH1m89gbVfH8NHXxVjUM9MXDo8DwN7ZLa74Ov2BvDlrjJ8+u0JlNW4YTXpMGVUF1wyNA+dIliwJjoTFpRbSbCkQ/bUQa4rg5DGxWqIiM5HluWE/MOvvVS4JAElIdFgQdDvUToGEbVCMp1neY7l603J5f2NR/D+xiOYMCQXcyb1TviffUEQ0K97Bvp1z0BNvRefby/Bf7eV4I//3oFOqUZcMjwPFw3u3OqWFMXlzsaWGrvL4A9I6NE5BQ/MGY7CPFvUW2oQNWFBuZU02b0BAKGy/RBZUCYiOidR1CAUCkKrjf8+XZESCPih0fC0SolNNJohO9m3jijeJdt5NhQKQhSTt0jC15uSydrNx/Du54cwekA25k4pVF27howUI66+qAemj+2Ob/dVYv23J/DvTw9ixeeHMapfFi4Zno8enVsu4hcISvhmbwXWbz2BA02L7PXPxqXD89A9J4W9hynm+M63lcS0HMBgQaj8AHSFE5SOQ0QU10wmK5zOWqSlZYb70KuVLMsIBPyora2EzZaudByiDhENZqC6XOkYRHQeyXWeleB0OmAynX2lebXj603J4rOtJ/DWJ/txQV87bp/WD6KormLy6bQaESP7ZWNkv2wcr3Th029PYOPuMny5qwzdc2y4ZHgeRvXLhtMdwGfbTmDD9hLUuwPISjfhhkt7YdygxFhkj9SLBeVWEgQRmuxeCJXvVzoKEVHcs1pT4XBUorz8OID2X7YoiiIkSYpcsCjRaLSw2dJhMlmUjkLUIeY+IxGw5ikdg4jOIxLn2UQ5xwIC9HojrNZUpYMopr2vd+K8xqfj652svtxZitc+2ovBPTNx11UDoBHV/eHJ6fLtVvxgSl/MvrgnNu4qw6dbT+CVD4vw1icH4PUHAQBDenbCpcPz0J+L7FGciGpB2eVy4cYbb8Rf//pX5Ofnh+/fs2cPFixYEL5dU1OD1NRUvP/++1ixYgWee+45ZGZmAgAuvvhiPPDAA9GM2Wqa7N4IVB2FHPRB0BqUjkNEFLcEQUBGRlaH98NLt4hiyzZwArzZ/J0jineROM/yHJs42vt68zWmRLG5qAL/+HAP+nVPx0+uGQitJnmKyaczGU4t4rfvWC2+2FmKNKsBE4d2RqdULrJH8SVqBeXt27fjkUcewZEjR1ps69evH1atWgUA8Hg8uO6667Bw4UIAwK5du7BgwQJMnz49WtHaTT/kCuiHTkv4hvBEREREZxJqqEOo/BBEew8ISTQziIiIiJSxbX8VXn5vN3rlpeKnswZzUTk0fojUt2s6+nZlOz2KX1F7p7Bs2TI89thjyMo69yepL730Ei688EKMGDECALBz506sWLECM2bMwC9+8QvU1dVFK2KbCaIWgiBAlhPtsiEiIiKi83N99yXcq34L2edSOgoRERGp3O7DNXhx5U50zbbi/uuGwKBnMZkoUUStoPzEE0+Ei8Rn43Q6sWzZMtx7773h++x2O+655x689957yM3NxaJFi6IVsV08n/0dng+eUToGERERUcSJxpN9wP1uZYMQERGRqu0tduD55TuQm2nBA9cPhcnAJb6IEomiv7HvvfceJk2aFO6XDAAvvPBC+Os77rgDl19+eZv3m5kZuRVh7XZbs9vVaamoP/QVOmUYIWgSa0XN748lUallHIB6xqKWcQDqGYtaxgGoayxE8U40mAEAst+jcBIiIiJSq4MldfjjOzuQmWrEz28YCqspsWorRKRwQXndunW46667wredTieWL1+OefPmAQBkWYZG0/ZLHqqrXZCk9q12fLozLWIQSOkGOehHedFuaLJ6dvgYsaKWBRnUMg5APWNRyzgA9YxFLeMAEn8soihE9ENOomgTjSwoExERUfQcLXNi8dvbkWrW4xc3DkOKRa90JCJqB8VWW5FlGbt378awYcPC95nNZvztb3/D9u3bAQCvv/56u2YoR5MmpzcAIFS2X+EkRERERJElGhpbXsi+BoWTEBERkdqcqHThube3wWTQ4BdzhiLdZlA6EhG1U0wLynfeeSd27twJAKipqYFOp4PBcOofEI1Ggz/+8Y9YuHAhrrzySuzevRvz58+PZcTzEs1pEGx2FpSJiIhIdTTmFIj2AghavsEjIiKiyCmvcePZt7ZBoxHwiznD0CnVpHQkIuqAqLe8WL9+ffjrJUuWhL/OzMzEl19+2eLxI0aMwIoVK6Idq0M02b0g1RxXOgYRERFRRGltGbBc85jSMYiIiEhFqmo9eOatrZBkGb+cMxzZ6WalIxFRB3EZzXYwXjQP0LLPDxEREamTLMsQBEHpGERERJTgHE4fnnlrK7y+EB68aRg6d7IoHYmIIkCxHsqJTNAZIAgCZLnjC/8RERERxRPX6/fDt+kNpWMQERFRgqtr8OOZN7fC6Q7gZzcMRddsm9KRiChCWFBuJ/eHz8K3canSMYiIiIgiSxAh+91KpyAiIqIE5vIE8NxbW1Hj9OL+64agR+cUpSMRUQSx5UUHhEqLlI5AREREFFGCwQz4PUrHICIiogS17UAVlq7dh7oGP+6/bjD6dElTOhIRRRgLyu2kyekN/5aVkH0NEAzsAURERETqIOjNkH2coUxERERt43D68Ma6ffhmbyU6d7LgwZnD0CsvVelYRBQFLCi3kya7NwAZoYqD0HYZrHQcIiIiosjQmyA31CqdgoiIiBKEJMn4dOsJLP/vQYQkGddO7IEpI7tCq2GXVSK1YkG5nTRZPQBBRKj8AAvKREREpBqC3gzJUaJ0DCIiIkoAxeVO/GtNEQ6XOjGgIAM/mNwHWelmpWMRUZSxoNxOgs4IMbMLpJoTSkchIiIiihjjxNsAUaN0DCIiIopjXn8QKzccxrotx2E1afGjq/pjVL9sCIKgdDQiigEWlDvAPHU+wP7JREREpCKCRqd0BCIiIopjX+8uwwvvbENNvQ8Th3bG7It7wmLk3w9EyYQF5Q4QjFalIxARERFFVODwFgR2fQzTFT+DoDMoHYeIiIjiRE29F2+s249v91Uir5MFv7plAHrnpykdi4gUwIJyB8gBL9wfPA1dn/HQ979U6ThEREREHSZ7nAiV7oXsd7OgTERERJAkGZ98exzvfn4IkiRj7tR+GD8gm4vuESUxFpQ7QNAZITfUIlRSBLCgTERERCogGBoX0pH9bsCSrnAaIiIiUtLRssZF946UOTGwIAO3TOmLAb2zUFnpVDoaESmIBeUO0mT3Qqj8gNIxiIiIiCJC0Jsav/B7lA1CREREimladO/jLcdgM+vx45kDcGFhFhfdIyIALCh3mCanN4KHvobkqoZozVQ6DhEREVGHCPqTM5R9boWTEBERkRK27qvE0nX7UFPvw8XD8jB7Yg+YuegeEZ2GBeUO0mT3BgCEyvZD7MWCMhERESU4/WktL4iIiChp1NR7sfTjfdi6vwr5dgt+/IOB6JWXqnQsIopDLCh3kJjZBdAaEKo6Al2v0UrHISIiIuoQ0dYJpmkPQszIVzoKERERxcimXWV4de1eyJKM6y7uicsv7MJF94jorFhQ7iBB1MBy/ZMQuGgNERERqYCg1UOb11/pGERERBQjkizjjXX70DnTjB/PHAh7mknpSEQU5/hxUwSI1gw2piciIiLV8G17H8Hi7UrHICIiohg4XuFCgzeIS4fns5hMRK3CgnIESLWlaFj5GwRL9igdhYiIiKjDAjs+YkGZiIgoSRQV1wIA+nXjlddE1DosKEeAYEqBVHEIobJ9SkchIiIi6ji9iYvyERERJYmiow5kpZmQkWJUOkrMyJIE7/G9kGVZ6SgJS/LUI1R+QOkYpBAWlCNAMFggpuchVLZf6ShEREREHSbozZB9LCgTERGpnSTJ2HusFoXd0pSOElP+7R+i5F8PIfDdeqWjJC5ZgufjP8O3ZQVkWVI6DcUYC8oRosnphVD5QcgSf4mIiIgosQl6E+D3KB2DiIiIoqy4wgmPL4i+XZOn3YXkqob/2/cAQYRv83LIvgalIyWUwKHNkLxOCAYLNPkD4P92FbzrXoQc9CkdjWKIBeUI0WT3BgIeSLUnlI5CRERE1CGC3gyZBWUiIiLVKzpaCwAoTKKCsm/jG4AA5Nz4MIwTfgjozUpHSgiyLMG3eTm8616Af9sHEDQ6GCfeAcPoGxA8/A3c7/0OkqtG6ZgUIywoR4gmpzcAIFR+UOEkRERERB2j7TUaugGXKh2DiIiIoqyo2IHsDDPSbQalo8SEHAoCWj30w66CucdQ6HpcCEEQIAc4u/Zc5IAP3o9fgH/raugKJ8Bw4WwAgCAI0A++EqYr/h+kunK4Vz/Z+D0m1dMqHUAtBJsdluufhJCao3QUIiIiog7R9bhQ6QhEREQUZSFJwr5jtRjdP1vpKDEjaLQwXXpXs8X4vJveRKi0COarH4Mgct7l90muang++j9INcdgGDMHuoGTIQhCs8douw6FeeajkOsrIGi0kGW5xWNIXfibEiGCIEBMy+UvDBERESU8yVWNYPEOrg1BRESkYkfLXPD6Q0nTP9n/3XoEDn4NAM1qN5qsnpCqjiKw51OlosW1wN4vINVXwjTlAegHTTlr3UuTkQdt92EAAN+Xrzf2p+ZifarFgnIEBUv3omHVbyG5a5WOQkRERNRuwUNb4FnzByDAPspERERqtbfYAQAo7JqmbJAYkJyV8G16C8FDX7fYpu1xITR5/eHbvBySp16BdPFJclYCAPTDZsBy7SJouw5u1fNkSQJCAfi3rm5crI/tRFSJBeUIEkQNpPIDCJXtVzoKERERUbsJhsbFaWSfW+EkREREFC17ih3IzTQj1ar+/sm+TW8CAmAYM6fFNkEQYBh3CxD0wffVvxVIF19kWYLv63fQ8PavEKo6AkEUIabYW/18QRRhmPBDGEbPQfAIF+tTKxaUI0js1A3QaBEqP6B0FCIiIqL205sAALKfBWUiIiI1CoYk7D9Wh8Ju6m93ESzegeCRb6EfdhVEa+YZH6NJ6wz9oCkI7tuQ1DUdOeCF9+M/w7/tfej6jIOYnt+u/TQu1jcFpin3Q6ovh3vF4wjVHItwWlISF+WLIEGjg8beA6FyzlAmIiKixCXoT85Q9rPlBRERkRodKXPCFwihUOX9k+WgH96Nr0NIzYF+8JRzPlY//CpAo4OYnhejdPGlcfG9P0KqOQ7DmJugG3h5h9cJ03YdAvPMR+H76m2IlowIJaV4wBnKEabJ7gWp6ijkoF/pKERERETtcqqgzBnKREREatTUP7mvyvsnyw0OCIII47hbIGh053ysoDPCMOIaCHoTZCkUo4TxQZZCcH/wNKT6KpiueAD6QZM7XExuosnIg/nKn0EwWCC5auDb+j4X61MBFpQjTJPdG5BCCFUeVjoKERERUbsIJhs0eQMgnGx9QUREROpSdNSBPLsFKWa90lGiSkzNhvm6J6DNH9jq5wSOfIuGtx5MmgX6ZFmCIGpgHDcX5qsfhbZL6xbfa4/AgY3wb34H3o9fgBzwRu04FH0sKEeYJq8fzNf9Dpqc3kpHISIiiltf7CjFis+Stz9dvBOtmTBPmw9t535KRyEiIqIIC4Yk7D9Rp/p2F76t70OqK4Mgatr0PDEtB7K7VvUL9DUuvvdveD/7O2RZhjZ/ADTpnaN6TP2QaTCMmYPg0W9PLtZXHdXjUfSwoBxhgs4ITXpnCAK/tUREFHurV6/G1KlTMXnyZCxduvSsj/vss89w6aWXxjBZc3UNPvxj9W5s2F6iWAY6N1kKsoUXERGRCh0qqYc/IKFQxe0ugsU74N/8DgKHv2nzc5st0FemzjWy5IAX3rXPw7/tAwhaHRCjFhSCIEA/aApMUx6AVF/ZuFhfEi+CmMhY9YyCwKHNcP/nD5BlWekoRESURMrLy7F48WK88cYbWLlyJd5++20cONDyD7Sqqio89dRTCiQ85YpRXTG0tx2vrd2LgyV1imahM3P98x74tryrdAwiIiKKsL3FDggA+qp0hnLTQnxiag70g869EN/Z6IdfBcGSAe+Xr6mun7LkrIJ71RMIFm+DYezNMIy/tc2zuDtK23UwzFc/CmgNCJbujemxKTJYUI4C2e9G6NgOSHWlSkchIqIksnHjRowePRppaWkwm82YMmUK1qxZ0+JxjzzyCO69914FEp6iEUXM/8EIpFkNeOHdnahz+RTNQy0JejPARfmIiIhUp6i4FvlZVlhN516kLlH5d6yBXF8Bw7gfQNBo27UPQWeEYcyNkKqLEfju0wgnVE6o5jjcKxdBclXBdOXPoR94ecQW32srTXpnWK5dBP2QqQCAYNl+LtaXQNr3m0Xn1NQ/OVS2H5q06PafISIialJRUQG73R6+nZWVhR07djR7zKuvvor+/ftjyJAhsY7XQopFj3tnDcLvXvsGL6zchQfnDINWw8+644WgN0H2e5SOQURERGfg3/MZtJ37QUzNbtPzAkEJB07U4eKheVFKpizJWQn/1tXQ9rgQ2vwBHdqXtuBC6AZfAU1unwilU55os0OT0wf6C2fFRb2qaQHoUM0xeFb/DmU7BiJozQlv12R0ga5wAuSAD77N77R8vtYAw8jZAADftg8gu2tbPEY/ZCpESzoChzYjVLavxXZtwQhoc/siVF2MwN4NLba3N0OVSQ+vxx/zDLHCgnIUiKm5gMECqfwAUDhR6ThERJQkJElqNsNAluVmt/ft24e1a9fin//8J8rKytp9nMxMa4dynu6CgZ3x/26U8Mzr32DFl0dwz7XKF7rbw263KR0hYprG4rfYIMr+hB1bouY+E7WMRS3jANQzFrWMgyjZyLKMwK518G/7AJYbnoIgtv4D+UMldQgE1ds/OVR5pHF28eg5Hd6XIAgwjr4RQMu/qxOJLEnwf7sKuj7jIKZkwXS5slcqnomYng/DmJvg27YaUsmpln1y16HQFU4AQgEE9n3R4nmCwRIupAaPboXkONHiMbrCiwFLOkIVh864DzGtM5DbF7Kz+ozb25shJAiQTrbCjWWGWGFBOQoEQYAmu5dqm7cTEVF8ysnJwZYtW8K3KysrkZWVFb69Zs0aVFZW4tprr0UgEEBFRQVuuukmvPHGG206TnW1C5LU8XUC7HYbKiud6JefiitGdcV/Nh5BdqoRE4YoP1uiLZrGoQanjyUo6CG7nAk5NrW+JolMLeMA1DMWNYxDFIWIfshJlCgEQYB+2HR41/8VgaL/Qt//klY/t6i4FgKAPiotKOt6XAht18EQtIaI7VOqr4TnkxdhGHMTtCevSE8Ust8Dz/qXECreBmi0MAyboXSkMxIEAfqBlyPvkllnPDcJRits8/5yzn1YZj5yzu3G0TfAOPqGs27Xdh92zmO0NcOZzrOxyBArvK40SjQ5vSHVlUHyJvYfaURElDjGjh2LTZs2oaamBh6PB2vXrsWECRPC2++77z589NFHWLVqFV5++WVkZWW1uZgcLbMn9sSA7ul4fe1eHDzBRfrigWCwAuACw0RERPFG9jUAsgQhNRu+ze+0qe5QdNSBrtk2WIzq6p8sB/3w7/gIctAf0WIyAAimFMjuOvi+fDWhFuiTnJVwv/cEQsd2wDD2FuiHTlc6EqkIC8pRous9DubrnoBgsCgdhYiIkkR2djYeeOABzJ07F1dffTWmT5+OwYMH484778TOnTuVjndOoijgrpkDGxfpW7ETtVykT3HGS++C5ZrHlI5BRERE3yPVlcH76cvQ9Z0I+L3wf/3vVj3PHwjhYEkdCrulRTegAvzb/wPf/95EqOJgxPct6AwwjJkDqfoYAt+tj/j+oyFYtg/uFYsguaphuvJn0A+clLAtOyg+saAcJaIlHZr0PAgCv8VERBQ7M2bMwPvvv4+PPvoId955JwBgyZIlGDRoULPH5efnY/36+PqD2GrS4afXDobbF8SLK3YhGOIqz0rimw4iIqL41LRorianF3SDLkegaAOk2vOvj3HwRB2CIRmFXdOjHTGmpPpK+Le9D22PkdB27heVY2gLRkCTPxC+Le9Ccsf/1XRSdTFgMMNy9a+hzR+odBxSIVY7o8j/3afwfvma0jGIiIgSRpcsK26b2g8HTtThjY9broBMsRPYvxENyx+FHAooHYWIiIhOI/vdAABBb4Zh+EyYpv4CYlrOeZ9XVFwLQQB656dFOWFs+Ta9AQgiDCcX0IsGQRBgHHsLEPTD9/WyqB2nI2RJQvDEdwAAXf/LYJm1CGJarsKpSK1YUI4iqa4MgaLPIYeCSkchIiJKGCP7ZePK0V3x2bYS/Hdby5WaKTZkvxtS9bHwLCgiIiKKD7KvqaBsgqA3QZs/ALIsQ3JVn/N5RcUOdM+xwWzUxiJmTASPbkPw6FYYLpgJ0ZoR1WOJaTnQj5gFTU6fqB6nPSSfB561/wfPB08jVHUUgiBA0EW2lzTR6VhQjiJNdi8gFIBUdUTpKERERAnl2gk9MbAgA6+v3YcDXKRPEYLe3PjFyTetREREFCdOftgbPlcD8H+zAg3vPArJU3/Gp/gCIRwqqVddu4tg8XaIabnQDZwck+MZhk6DvnBiTI7VWpKzEif+9RBCx3bCMO4H0HTqpnQkSgJRLSi7XC5Mnz4dx48fb7Htz3/+My655BLMnDkTM2fOxNKlSwEAJSUluPnmm3HFFVfg7rvvRkNDQzQjRpUmpzcAIFR+QOEkREREiUUUBfzoqgHISGlcpM/h5CJ9sdb0JrXpsloiIiKKD0JqNrQ9RgI6Y/g+bc9RQMB31gX6DhyvQ0iSUdhNXQVlw/i5MF/1MARN7GZdy6EgvF+8Cv/udTE75tk0Lb4XclbDdOXPoR9wmdKRKElEraC8fft2zJkzB0eOHDnj9l27duEPf/gDVq1ahVWrVuHmm28GADz++OO46aabsGbNGgwcOBAvvvhitCJGnWhOg2CzI1S2X+koRERECcdq0uGnswbD6wvhxZU7EQhykb6Y0psAgC0viIiI4oyu+3CYJt3TbAFdTXoedIMmI7B3wxkntRUVOyAKAnrlpcYyatRI9ZUIHt/V2NrBaI3twUUNpPoK+L5erugCfbIUhPfTJYDBgs7zfg9t/gDFslDyiVpBedmyZXjssceQlZV1xu27du3CSy+9hBkzZmDRokXw+XwIBALYvHkzpkyZAgCYNWsW1qxZE62IMaHJ7oVQ+X7Isqx0FCIiooSTn2XFbdP64eCJeryxjov0xZJg4AxlIiKieCS5a8/Y2sIw/CoI5jR4v3gNstT8g/iiYgcKcm0wGdTRP9m36Q14Pv4zZK8r5scOL9AX8sP3VewX6JMlCbLfA0HUwjTlfliufhT6zM4xz0HJLWoF5SeeeAIjRow447aGhgb069cP8+fPx4oVK1BfX48XX3wRDocDVqsVWm3jP3B2ux3l5eXRihgT+mEzYJr2S6VjEBERJawLC7MwdXQ3/HdbCT7jIn0xI6Zkw3zNQs52ISIiijO+Df+C58NnWtwv6E0wjJkDqfoogvu+CN/v9QdxpNSpmnYX4YX4hl8V+9nJJ4lpOdAPvhLB/V8iWBa7SQ+y/+Tiex//GbIkQZORB8FgidnxiZoo8tGUxWLBkiVLwrdvu+02PPTQQ7jpppuaXbIBoMXt1sjMjNw/KHa7rYM76BuZIBHQ4bHECbWMA1DPWNQyDkA9Y1HLOAB1jYXab9aEHiiucGLp2n3I72RFr3x1XK4ZzwStHhp7d6VjEBER0ffIfnezBflOp+0xEsagv7Gn8kn7m/onq2BBPjnoh3fjUohpnWO2EN/Z6IfNQGD/Rvi+eA2aWQshiJqoHk+qr4Dno/+DVFsKw7hbIIhRXRaN6JwUKSiXlJRg48aNmD17NgBAlmVotVpkZGTA6XQiFApBo9GgsrLyrC0zzqW62gVJ6niLCbvdhspKZ4f349v6PgSNDvrBUzq8r/aK1FiUppZxAOoZi1rGAahnLGoZB5D4YxFFIaIfciYzURRw11UD8Jt/bsELK3bi1/MuRLrNoHQs1fP+721ocnpB1/0CpaMQEcWF1atX4y9/+QuCwSBuvfXW8FpATf773//i2WefBQD06dMHixYtgsXC2YMUWbLfDdHa6YzbBEGAru9FjY/zuiAYrSgqdkAjqqN/sn/7h5CdlTBOezCmC/GdiaAzwDD2JoSKtwNBf3j9iWgIlu6F9+M/Q5YlmKb+Atq8/lE7FlFrKPJxhtFoxDPPPINjx45BlmUsXboUl19+OXQ6HUaMGIEPP/wQALBy5UpMmDBBiYgRFSotQmDvBqVjEBERJTSLUYd7rx0Erz+EF1dwkb5YCBR9htCJPUrHICKKC+Xl5Vi8eDHeeOMNrFy5Em+//TYOHDi1+Fl9fT0WLFiAxYsXY/Xq1SgsLMTixYsVTExqJfvc5y1eBk98B9cbP0Oo/ACKjtaioHMKDProzqCNNlkKInBgE7Q9R8VNQVVXMALGibdDiGIxOVR+AJ4PnoZgsMBy9aNxM3ZKbjEtKN95553YuXMnMjIysGjRItx999244oorIMsyfvjDHwIAHnvsMSxbtgxTp07Fli1bcP/998cyYlRocnpDcpyA7GtQOgoREVFCy7dbcfu0fjhYUo+lH3ORvmgT9GbIfo/SMYiI4sLGjRsxevRopKWlwWw2Y8qUKc0WkT9y5Ag6d+6MXr16AQAuueQSrFu3Tqm4pGKy33PeAqbGXgDBYIF7w6soLqtTRbsLQdTCMmsRDONuUTpKC4EDm+Dd9GZU9i3aC6AfOh3mqx+FmJoTlWMQtVXUrw9Yv359+OvT+yZPmTIFU6a0bAGRl5eH1157LdqxYkqT3RuAjFDFQWi7DFY6DhERUUIbUZiFaWO64YNNR9E9x4aLh+UpHUm1BL0Z8LuVjkFEFBcqKipgt9vDt7OysrBjx47w7e7du6OsrAxFRUUoLCzEf/7zH1RVVSkRlVRMlmUIJhsEy7kLxILeBMPoOfB+8iLG6PeiX9dhMUoYHaGKQxAs6RAt6RB08df2THKUILDzI4QqDkLAybXADBaYr7gfAOD55K+QXdUtnme89C6Itk7wb/8QwSNbm22T/W4YJ94OTVYPGEZcE+0hELWJsg1nkoQmqwcgiAiV7WdBmYiIKAKuuagHistdWPrxPuTZLeidn6Z0JFUS9CbILCgTEQEAJElqtmi8LMvNbqekpOCpp57Co48+CkmScP3110On07XpGJFee0EtixurZRxAhMZy74utepjc6VJ8+9UaTJO3oUefH8GcGrnvYyxfEyngw/Flf4XGnIq8H/4+ovuO1Diky+egKtSAUH1l+D7RaArvv8JiQihkbPG8zEwrtKk21KVY4DZ9b7vZBCucsLYyo1p+T9QyDkBdY/k+FpRjQNAZIWZ2Qaj8wPkfTEREROcligJ+dFV//OZfW/Diil1cpC9a9CbIDQ6lUxARxYWcnBxs2bIlfPv7i8iHQiHk5OTg3//+NwBgx44d6NKlS5uOEakF5oHEX9y4iVrGASgzlpW+0ZgrvIXqda+iYcJtEdlnrMfh27ICwdoK6Mb/MKLHjfQ4hNFzWxTZmvYvjL71jAU4hx9ApRMomAhtwcQW2z0APK3IqJbfE7WMA0j8sZxvgXlFFuVLRoaxt8AwNv76/BARESUqi1GHn84aBG8ghBe4SF9U6PtdAv2QK5WOQUQUF8aOHYtNmzahpqYGHo8Ha9eubbaIvCAIuO2221BeXg5ZlvHPf/4TU6dOVTAxqVHIUQLX0p8heGzneR/r9gawvUKLotwZ0A+ZFoN0kSfVV8C//QNoe46GtnM/peMQ0UmcoRwj2pzeSkcgIiJSnTy7FXdM64cXVuzCH/+9HXmdLDHPYDLr4XH7Y37caLh8THfYrfrwbW23ocqFISKKM9nZ2XjggQcwd+5cBAIBzJ49G4MHD8add96J++67D4MGDcKiRYtwxx13wO/3Y8yYMbj99tuVjk0qI/tckBtqgNParZzN3mO1kGUgfcjFEFPTIYeCgCBAEDUxSBoZ3o1LAVELw+gblI5CRKdhQTlGZFmG/9v3IJhSoO9/idJxiIiIVOOCvlmYfXFP/Od/R3G0LPaXlQmiADlClycryesPodYdwD0zB4Tvk2rLEKo6Am3PUc36hBIRJasZM2ZgxowZze47ffH5iy++GBdffHGMU1FSObm2gaA3n/ehe4trodWI6NE5BZKnHp73fgfdgEnQD5wU7ZQREXKUIHRsJwyjrod4nkUIiSi2WFCOEUEQECrZA9lTB12/i/mmjIiIKIKmju6GqaO7KXLsRO+P1uRP7+xAVa2n2X3Bo9/C99UyWLsNBXQtF5IhIiKi2JL9jedqQW8672OLjjrQKy8FOq0GssYGwZoJ35bl0PYcCdGUEu2oHaZJ7wzz7N9CTM06/4OJKKbYQzmGtD1HQqothVRzXOkoMSFLEmQpqHQMIiIiaoU0mwHVdd7md56c/ST73AokIiIiou8Ln5PPM0PZ5QngWIULhd0aZ/YKggDDuJuBoB++r5ZFO2aHhcoPQJZC0KR3hiByLiRRvGFBOYa0BSMAQUTw4FdKR4kJ/+Z34F7+GGQ58S8DJiIiUrt0qx5Otx/+QCh8X9PltE2zoYiIiEhZrZ2hvLe4FjKAwq6nWkVo0jpDP/gKBPd9gWDZ/mjG7BCprhzu1b+Hf/NypaMQ0VmwoBxDoikFms79EDj4VVIUWYWULEiOE5CqjyodhYiIiM4j3dbY0qLW5QvfF36z6ucMZSIionigH3g5LHOegaDVn/Nxe4sd0Gsb+yc3e/6wqyBYMuD78lXIkhTNqO0iy3LjQnwaLXQDL1c6DhGdBQvKMabtORKys1L1bS+k2lLoCkYAogaB/ZuUjkNERETnkW4zAAAcztMKyoamGcosKBMREcUDQWeAaLOf93FFxQ70yk+FVtO87CPoDDBeNBf6YTOAOFzbKXh0K0LHdsBwwdVciI8ojrGgHGO6HiNhvu530GR2UTpK1ITKD6Bh2a8QPL4L2i6DETz4VVx+8klERESnpJ2poGxOg7ZgBASjTalYREREdBr/jjXwff3vcz6m3u3H8cqGZu0uTqftOhS6HiMhCEJcvVeXgz74Ni6FmJ4H3cBJSschonNgZ/MYE/QmaFqxGmsi821ZAcFog7bb0Mae0Ue3IlRaBG1ef6WjERER0VlkNBWUT2t5IVozYbr8XqUiERER0fcET+yG7HXBcI7H7CuuBYDwgnxnIssyvP/9GwABpovviGjG9grs+xKyqxrG6Qu4EB9RnOMMZQWEHCfQsPwxhMoPKB0l4oKlexE6sRv6oVMh6IyNRWWdCaHKQ0pHIyIionMwGbQwGTTNZijLsgzJUw/Z16BgMiIiImoi+z3hRXPPpqjYAYNOg+45Z7/CSBAEiOa0uFqgT9fvYpimL4C2c6HSUYjoPFhQVoBoyYBUewKBg18pHSWiZFmGf/NyCOY06PpfCgAQtHpYb34OhqHTFU5HRERE55ORYmpWUAaAhqU/g3/bBwolIiIiomZ87lOL5p5FUXEtep+hf/L3NV+gLxTJlG0iyzJCjhIIgshiMlGCYEFZAYLe1Nhb+NBmyHL89CvqqNCJ3QiV7YN+2HQI2lMX4Ah6M2RZhuz3KJiOiIiIzqdTmhG1p/dQFgQIehPP4URERHFC9rvPOUO5rsGPkqqGc7a7aCLoDDCMmQOp+hgC362PZMw2CR7dCve/H0Lw+C7FMhBR27CgrBBtj5GQ3bUIxcmlJZGgsRdAP3I2dIUTW2zzrH4S3s/+pkAqIiIiaq3MVFOzHsoAAL2ZBWUiIqI4Ifs9wDlmKO8tdgDAWRfk+z5twQho8gfCt/ldSO66iGRsi1ML8eVDw9nJRAmDBWWFaLsNBTR6BFXS9kKWZQgGCwxDp0PQ6FpsFzt1Q7B4O3swEhERxbHMVCNqnX5Ikhy+TzCYIfvdCqYiIiKiJsaLb4eu95izbi8qroVRr0G3HGur9icIAoxjb4Gu30QIWn2kYraaf+v7kF3VMIz/ARfiI0ogLCgrpGnBOqn6mNJROkyWJXg+eBr+PZ+d9TG6XmMAKYjg4W9iF4yIiIjaJDPVBEmWUe/2h+9rbHnBgjIREVE80PUYCU2n7mfdXnTUgT5d0qARW1/uEdNyYBx9IwS9CcET30Gqr4hA0vOT6srh3/4faHuNgTa3b0yOSUSRwYKygowTfgjTVQ8pHaPDgoe/QahkzxlnJjcR7QUQUrIROLAphsmIiIioLTJTjQDQbGE+wZLebG0EIiIiUobsa4B/9ydnLfg6nD6U1bhb3e6ixf5DQXj/+3e4VyxCsKSoI1FbxbflXUCjg2H0DVE/FhFFFgvKChL0JgiCkNCzfmRJgn/LCohpudD2OvtlN4IgQNdrNEIlRZAaHDFMSERERK11poKy6eI7YZ42X6lIREREdJLkrILvy9cQqjnzlc57j53sn9wtrV37FzRamKfNh2C0wvPBM/AX/be9UVvFOO4HME3+KURzWlSPQ0SRx4KywnxbVsD15nzIoaDSUdolePB/kGpLoB9xDYTzXFKj6zUGYqeukBVo9E9ERETn1ym1cZGf0wvKREREFB+aJqMJevMZtxcdrYXJoEXXLFu7jyGm5sB89aPQ5PWD7/NX4N30JmRJavf+zkQO+iB7XRCMVmjz+kd030QUGywoK0xj7w74GhA6sVvpKG0mS0H4vlkJMbMLtAUjzvt4MS0HllmPN46ZiIiI4k6q1QCNKKDWdaqg7P9uPVxv/gKyFFIwGREREZ23oFzsQN8uaRBFoUPHEQwWmK54ALoBkxDY/Qmk2hMd2t/3+be+j4Zlv4LsdUV0v0QUOywoK0yTPxDQmxA4+LXSUdpM9jZAtNlhuGAWBKH1P0qhmuNse0FERBSHRFFAqlWPmvrTZiiHApCdVYDfo1wwIiIiCp+LBb2pxaaaei8qHB4Udk2LyKEEUQPjuFtgue4JaDK6QJYkSK7qDu9Xqi2Df/t/oOkyCILRGoGkRKQEFpQVJmh00Ha/AMEj30IO+s//hDgimlNhnjYfmm5DW/0cyVMP9zuPIrDns6jlIiIiovZLtxmazVBumgWVyGs+EBERqYHsO/sM5b3FtQCAwm7tW5DvbMTUbACA/5sVaFj+6w4t1ifLMrwbX29ciG/U9ZGKSEQKYEE5Duh6jgQCHgSP71I6SqsFjnyD4InvADQuuNdaoikFmrx+CBzYBFmWoxWPiIiI2indamjeQzlcUOYMZSIiIiWJ6XnQFV4MnGGG8p5iByxGLfKzojPrV9dnPERTCjwfPIP6rR+3ax/BI98gdHwXDCOu4UJ8RAmOBeU4oMnrDzGzGxBMjAVw5KAfvi9eg/+ble0qCut6jYFcXwGp8lAU0hEREVFHpNkaC8pN5/imy2o5Q5mIiEhZ2vwBME6YB0HUtNhWdNSBPl3SILZhwldbiKnZ4cX6qj78K7wb32jT+gqyFIRv05sQM/KhG3BZVDISUeywoBwHBFELy7WPQ9drjNJRWiXw3XrI7lroL7y2TbOTm2gLLgA0WgQO/C8K6YiIiKgjMmxG+AIheHyNbxIFA1teEBERxQOpvhJSfUWL+6vqPKiq80a83cX3CXozTFc8gJQLpyGway3836xs/XNFLUyX3Q3jhNvOWBAnosSiVToAnSK564CAN9yjKB7JAS/82z6AJm8AtLl927UPQW+GtutQBA9+BXn0jTyZEBERxZE0mx4A4HD5YDZqIaZ1huWGpyBYovsmlYiIiM7N97+3INWVwXLdE83ub+qf3K9r9M/VgqhBp8m3wW/Ng7bLYACALEsQhLPPV5T9bkBngia7V9TzEVFscIZynJBlGe53H4Pv638rHeWc/LvWQfY6YRhxTYf2o+t/aeNlLqFghJIRERFRJKRbDQAAh9MLABC0eoip2RC0eiVjERERJT054Dlj/+Siow5YTTp0tltilkXXeywEoxWSuxbudx9DsGTPGR8nyzI8616E56M/ch0lIhVhQTlOCIIAbfcLECzeATngVTrOGcmyhOD+jdB0HdLhTxa1ef1hGD4Tgs4QoXREREQUCekpRgAIL8wnyzK8n/8DgUOblYxFRESU9GSfG8LJxXLD98kyioodKOwavf7J5xQMAKEQPB88C/+ez1puPrkQnzZ/YLtaZhJRfGJBOY5oe44EQn4Ej25TOsoZCYII8zWPwXjRvIjsT2pwwLd1NeQEWYyQiIgoGaRbG2ci154sKAuCgMCBrxAqP6BkLCIioqQn+z0tCsqVdV5U1/vQNwbtLs5ETLHDfPUj0OQPgG/DP+H98vXwYn1ywAffxjcgZnSBrv+liuQjouhgQTmOaHJ6Q7CkI3jwK6WjtCD7GiB56iHoDBAj1ENRqiuDf/NyBI9uj8j+iIiIqON0Wg2sJl14hjIACHoT4PcomIqIiIjgdzeek0+z96gDAKK+IN+5CHozTFPuh27QFAR2r4NnzWLIUgj+rashN9TAMP4HXDuJSGVYUI4jgiBCW3Ahgsd2xt1K6v5tH6DhrQche10R26cmpy8EcxqCBzZFbJ9ERETUcek2w/cKyua4+9uEiIgo2QgpWRBTsprdV1TsQIpFj86Z5rM8KzYEUYRxzBwYJvwQmpw+kJ1V8O/4D7S9x0Gb00fRbEQUeVqlA1Bzut5jASkIOehvcSmLUiR3Hfy710Hb/QIIRmvE9iuIIrS9RiOw62PIXldE901ERETtl24zwOE6rSWVwQyZM5SJiIgUZbn60Wa3G/sn16Kwa1rc9CfWF04EAMhSCIbRN0LbY6TCiYgoGjhDOc5o7N1hHD8XojlN6Shh/m0fAKEgDBfMjPi+db1GA1IIgcNbIr5vIiIiap806/dnKJs4Q5mIiCjOVDg8cDiV6598LoKogX7g5RDNqUpHIaIoYEE5Dsl+N/xF/41oe4n2klw1COxZD12fcRBTcyK+fzGzG8S0XLa9ICIiiiMZNgOc7gACQQkAoB90BQwXXK1sKCIioiQWqi2B85UfI3D4m/B9RcUn+yd3TVMoFRElK7a8iENSXQV8n78CyDL0/S5WNIt/6+rGHMOvisr+BUGAYdwPIJhsUdk/ERERtV2azQAAqHP50CnNBG3+AIUTERERJTmfGwh4IWhOlXGKimuRatUjJyM+2mUSUfLgDOU4JHbqBiElG8FDXysdBbr+l8Iwfi5Emz1qx9Dm9Ycmo0vU9k9ERERtk36yoFxzsu1FqLoY/j2fKZiIiIgouYXXMji51pIsyyg66kC/rulx0z+ZiJIHC8pxSBAE6HqORKhkDyR3nWI5ZFmGJrNLuKl+NAUOfQ3P+peifhwiIiI6v6aCcu3JhfmCxTvg2/BPyEG/krGIiIiSVlNBWdCbAABlNW7UNfjRl+0uiEgBLCjHKW3PkYAsI6jQYnVSbSncKxYiVH0sJseT3XUIHtiEUM2JmByPiIiIzq6poNy0MF/Tm9fw7CgiIiKKqabFcYWTM5SLimsBAIXd4m9BPiJSPxaU45SYng8xrbNibS9836yCVFsKIUYrsmp7jAQEkYvzERERxQGzQQu9TjxVUDac7M148s0sERERxVi4oNz4IW/RUQfSbQZkpZmUTEVESYqL8sUpQRBgGHkdoNXF/NihmuMIHvwK+qFTIZpSYnJM0ZwKTV5/BA7+D/oLr2UPKCIiIgUJgoB0q4EzlImIiOKEbtAV0BVOBHRGyLKMvcUODCjI5HtnIlIEZyjHMW33YdDmD4z5cf1bVgA6I/SDr4zpcXW9xkB2ViFUfiCmxyUiIqKW0m2nCsrQWwCcutyWiIiIYksQRQgGCwRBQIM3iHp3AN2yrUrHIqIkxYJynAse2wnfV8tidrxQ1REEj3wD/eApEIyxPTlpuw8HNHqEirfH9LhERETU0ukFZdGaAV3fCRBidOUSERERNefb9j68G/4F4NQaB+kpRiUjEVESY8uLOBeqOgr/9g+hG3AZRGtm1I8ne10QM7tBP2hy1I/1fYLeBMvsRRBSsmN+bCIiImouzWZArcsHSZYhWjNhnHib0pGIiIiSVqjsAOSGGgCnFZRPLqJLRBRrUZ2h7HK5MH36dBw/frzFtnXr1mHmzJm46qqrcM8996Curg4AsGLFCowfPx4zZ87EzJkzsXjx4mhGjHu6niMBAMGDsVmcT5s/EOZZC8Mrx8aamJoDQRAgS5IixyciIqJG6VYDQpIMpzsAWZYh1ZVDctcpHYuIiCg5BTzhNQ1qXScLylYWlIlIGVErKG/fvh1z5szBkSNHWmxzuVxYuHAhXn75Zbz33nvo27cvnn/+eQDArl27sGDBAqxatQqrVq3CAw88EK2ICUFMyYJoL0DgUHQLyrLXBd+WdyG5qhVv6u9Z+zy8ny1RNAMREVGyS7c1XkZb6/QBkNHw9gIEdq9TNhQREVGSkn3u8MSvmnovBACpVr2yoYgoaUWtoLxs2TI89thjyMrKarEtEAjgscceQ3Z2Y2uDvn37orS0FACwc+dOrFixAjNmzMAvfvGL8MzlZKbrORJS5WFI9RVR2X+o5hgaVjwO//YPEao8HJVjtIVgsiF45BvIAa/SUYiIiJJW02W0DqcPgiACehNkv0fhVERERMlJ9ruBkwXlWpcPKRY9tBoui0VEyojavz5PPPEERowYccZt6enpuPzyywEAXq8XL7/8MiZNmgQAsNvtuOeee/Dee+8hNzcXixYtilbEhKHt0dj2IhqzlAOHv4F75W+BUADmGb+CruDMr1ksaXuNAYJ+BI98q3QUIiKipHWqoNz4Aa+gNzW+mSUiIqKYk/2nWl44nH6ksX8yESlI0UX5nE4nfvKTn6CwsBDXXHMNAOCFF14Ib7/jjjvChee2yMy0Riyj3W6L2L7aH8IGzy2Pw5DXB6K2/Ze0nD4WWZZR+8W/4fz8bRg690b27AehtWVEIm2HyZ2Go/i/nSAUb4F97JQW2+PiNYkQtYxFLeMA1DMWtYwDUNdYiBJJqkUPURDgONmnUTCYAc5QJiIiUoRp0k8gmNMANH7Y2ynVpGwgIkpqihWUKyoqcPvtt2P06NF46KGHADQWmJcvX4558+YBaCx6ajSaNu+7utoFSZI7nNFut6Gy0tnh/USEuRtcDh8AX7uefqaxeKuroO09DrqLboXDqwO8cTJWAJqCkfDsWIPy4hMQTSnh++PqNekgtYxFLeMA1DMWtYwDSPyxiKIQ0Q85iWJJFAWkWvXhleQFvZkzlImIiBSizR8Q/trh9KF3lzTlwhBR0lOk4U4oFMKPf/xjXHnllXj44YfDi8CZzWb87W9/w/bt2wEAr7/+ertmKKuRLEnwfPJX+LZ92KH9SM5KBIsbv7+GMTfDePEdEDow6zlatL3HQDDaINWVKR2FiIgoaaVZDeGCspiaDeG0D3mJiIgoNmRfA3xbVyPkOAF/IIQGbxDpVra8ICLlxHSG8p133on77rsPZWVl+O677xAKhfDRRx8BAAYOHIgnnngCf/zjH7Fw4UJ4vV50794dTz/9dCwjxi1BFCG7HQhWH4V+yJXhInxbBEv2wPvxC4BGC8uNT8dlIbmJJqMLLDcvhiBykQEiIiKlZNgMKKluAAAYJ9ymcBoiIqLkJDXUwL95OcTUHDiQDuDUWgdEREqIekF5/fr14a+XLFkCABg0aBCKiorO+PgRI0ZgxYoV0Y6VkLQ9R8H3xauQao5Dk9ml1c+TZRl1W9bAs/YfEFOzYZpyX1wXk5sIogjJ64QAAYKRl4wTERHFWprNgO+O1igdg4iIKKnJvsaWU4LejNqTVw6xoExESuL0zwSiLRgBCAKCB79q9XPkUBC+Df9C9UdLoOkyEOarH4WYmhPFlJEjeZ1oeP1++Pd8qnQUIiKipJRuM8DjC8HjC8K//T9w/usnkOWOr1NBREREbXByUVxBbwq3omJBmYiUxIJyAhFNKdB07o/Aoa9b/WZOqi9H4MBGpI2dBdPk/wdBnzgrwYpGGzRZPRE8sIlvXomIiBTQ9Ga11nVyUWBfAxDwKpiIiIgo+TQtiivozeGCchp7KBORglhQTjDaniMhN9RCdlad83EhRwlkKQhNeh4sNzyFjEtuTsh+xNpeoyE5SiDVHFM6ChERUdJpWvDH4fQBBjOAU29qiYiIKDbC515DY0HZZNDAZIjpklhERM0kXoUxyel6jYZ17p8gptjP+pjAwa/hfnch/FtWAgBES3qM0kWetseFgKBBYP8mpaMQERElnaYZyg6nL3yVk3zyslsiIiKKDTGzG3SDr2ycoezycXYyESmOBeUEI2gNEHRGyKFAizYQsizBt3k5vJ+8CLFTV+gGTlIoZeSIRhs0XQYiePAryLKkdBwiIqKkktasoNw0Q5kFZSIioljS5vSGcfQNEDRaOJw+9k8mIsWxoJyAgmX74XrtPkgVB8P3yX4PPB/9Cf6tq6HrOwHm6b+EaE5TLmQE6QonQNtlEBDwKR2FiIgoqRh0GliMWjhcpwrK8DcoG4qIiCjJhBwlCFUdBQAWlIkoLrDpTgLSZOQBwQACB7+GJrsXAMC3eTlCx3bAMPZm6AZMgiAICqeMHF33C6DrfoHSMYiIiJJSms0AR70PYmZPWOY+f6qwTERERDHh/2YlQtXFMF/3JOpcfhaUiUhxnKGcgAS9GdougxA8vDl82anhwlkwTf8l9AMvV1UxuYns9yCw70vIwYDSUYiIiJJKus3QOENZo4VotEEQNUpHIiIiSiqy3w1Bb0Jdgx+SLIcXzSUiUgoLyglK23MU5AYHGt5eAMlV3Vhkzu2rdKyoCZXth/ezJXAf3Kp0FCIioqSSbjWg1umDLIXg+fjPCBz4n9KRiIiIkors90DQm1HramwDmW4zKpyIiJIdC8oJStttKARzGjTZvZLi0lNNfn8IRhtcuzcoHYWIKK6tXr0aU6dOxeTJk7F06dIW2z/++GPMmDED06ZNw4IFC+D3+xVISYkk3WZAfYMfQQkIHt0GqbpY6UhERETJ5eQMZYezqaDMGcpEpCwWlGNEkmXsOlyNnYeqcaSsHlV1HvgCoXbvT9AZYbnpOZgm/xSC3hTBpPFJELXQ9rgQ7v1bIIeCSschIopL5eXlWLx4Md544w2sXLkSb7/9Ng4cOBDe7na7sWjRIrzyyiv44IMP4PP5sGLFCgUTUyJItxkgA6hvCEDQmyD73UpHIiIiSipNM5SbCsppLCgTkcK4KF8MSLKMf/2nCBt2lLbYpteJsJl0sJr1sJl0sJl1sJr0jf8362A7+XXj/TpYTDqIJ3skJ1sPQ429AIHv1kNuqIGQkqV0HCKiuLNx40aMHj0aaWlpAIApU6ZgzZo1uPfeewEAZrMZ69evh06ng8fjQXV1NVJSUhRMTImgaRaUw+VDjsEcXr+BiIiIYkPM7AoxIw+Oah80ogCbWad0JCJKciwoR5kkyXjlP3vw5c4yTBvTDUN6dYLT7YfTHYDLE4DT7YfLHYDTE4DTHUBZjRsuTwBe/5lnLwsCYDHqThaZ9eidn4oRfbPQNduqysX4TidYMwEAkqsaIgvKREQtVFRUwG63h29nZWVhx44dzR6j0+nw3//+Fw8++CCysrIwfvz4WMekBJN2cuEfh9OHXL2ZM5SJiIhizHzlzwAAjtXfIc1qCE8yIyJSCgvKUSRJMv7+wR5s2l2Gq8cX4KrxBa1+biAYOq3oHIDTc7IIfbL47HL74XD58J//FeODTUeRlWbCiMIsXFio3uKymJaL1NEzETCnKh2FiCguSZLU7N9/WZbPeD6YOHEivvrqK/zhD3/AwoUL8dxzz7XpOJmZ1g5nbWK32yK2LyWpZRxAy7EYzI0F5YAMGKw2SH5fQow3ETK2llrGopZxAOoZi1rGQaRmsiwDsgxBFOFwetk/mYjiAgvKURKSJPz9gz343+5yXDOhB2aM7d6m5+u0GmSkaJCRcu7VW51uP77dV4kteyux5qtifPi/o7CnGcPF5W7ZNsWKy8GQBFEUIvbpqWhJR+Zlc1FZ6YzI/oiI1CYnJwdbtmwJ366srERW1qkrOmpra7Fr167wrOQZM2bggQceaPNxqqtdkCS5w3ntdpsq/k1XyziAM49FlmVoNSKOl9Zj5KAZEGUp7ser9tckEallHIB6xqKGcYiiENEPOYnikeysRMNbD8J4yY/gcInomsWfeSJSHgvKURCSJPzt/T346rtyXDuxB6aN6R61Y9nMekwcmoeJQ/Pg8gQai8tFFVj79TH853/F6JTaWFy+fHR3pBk1USsuB0MSTlQ24Gi5E0dK63GkzInjlS6k2wyYf+MwdEqLzMKB/oqjCFU7oenULSL7IyJSk7Fjx+L5559HTU0NTCYT1q5di9/85jfh7bIsY/78+Vi+fDk6d+6MNWvWYPjw4QompkQgCALSbXrUOL3Q5gxUOg4REVFSCbea0hpQ63RhSM9MZQMREYEF5YgLSRKWrP4OX++pwHUX98SVo2NX+LSadJgwpDMmDOkMlyeArfsqsXlvBT7efAxrvjpZXO6bhRGFWSjIbf/M5WBIQklVA46UOXG0zIkjZfU4VtGAYEgCAJgMWnTPseHS4fn4cmcpnnpjK3558zB0Su14Ubny/RcQ1JphvvLnHd4XEZHaZGdn44EHHsDcuXMRCAQwe/ZsDB48GHfeeSfuu+8+DBo0CL/5zW9w1113QRAE9OrVC48//rjSsSkBpNuMqHX6ECo/gFD5fugHX6l0JCIioqQg+xoLyn7BAF+gLry2ARGRklhQjqBgSMLL7+3Glr2VuP6SXrhiVFfFslhNOlw0pDMuOllcPljmwqdbivHxlmNY83UxMlOMGFFox4jCLPTITTlrcTkkSSipcuNIWX24gFxc7jqteKxBt2wbJl2Qj245NnTPtcGeZgq3uRg9IBvPvrkNT7+xFb+8aTgyU8/dwuN8NCmdEKg41qF9EBGp2YwZMzBjxoxm9y1ZsiT89aRJkzBp0qRYx6IEl24z4FBJHYLHd8P/zQroBk6GIGqUjkVERKR6st8DAKgPNJZvMlJYUCYi5bGgHCHBkISXVu3GN/sqceOlvTB5pHLF5O+zmnSYNLIrhhSko8EbwLb9VdhcVIF1W47jo6+PITPFgAv6NvZcNug1OFJ6+sxjF/zBxuKxUd9YPL7sgjx0y7GhICcF9nTTOXskd89Jwc9vHIpn39qGp9/8Fr+8afh5+0KfizbVDunQ9rMuNEVERESRl241wOH0A/qT53C/BzCyhyMREVHUnWx5URto/CCXM5SJKB6woBwBwZCEv6zcha37qzBnUm9cPqKL0pHOymLUYdygXIwblAu3N4Ct+6uwpagC6789jrWbT838NZwsHl88LA/dc2zolmNDdoa5XQvsFeSm4Oc3DMVzb2/F029sxYM3DWt3UVmbkgkEvI0nVYOlXfsgIiKitkm3GRAMSfCj8U2s7HdDYEGZiIgo6uSgDwDg8IgAGs/JRERKY0G5gwLBxmLytgNVuPnyPrjsgnylI7WauVlxOYgdh6ogS0D33PYXj8+mR+cU/Oz6oXju7W145s2tePCm4e06EWpT7AAAyVUNDQvKREREMdF0znaFtLDitAWCiIiIKKr0AyZB1+9SVG08AoAzlIkoPohKB0hkgWAIL6zYiW0HqvCDyYlVTP4+s1GL0f1zMGZgDnIzLREtJjfpmZeKn10/FLUNfjz95lbUunxt3ocuIxeavAERz0ZERERnl3ayoNzUv7GpnyMRERFFnyCKqHX5YTProNOyjENEyuO/RO0UCIbw/Ls7seNgNeZe0ReXDE/cYnIs9cpPxc+uH4Japw9Pv7EVdW0sKhuyu8M8bT40mfHTo5qIiEjtMk4WlKslK3SDr4BgTlU4ERERUXLwfbMKnk/+ghqnD+mcnUxEcYIF5XbwB0L40/Kd2H2oBvOuLMTFQ/OUjpRQeuen4YHrh8Dh9OHpN7eirsHfpufLUpCX2hIREcVQikUPAUCF3wzj6BuhSeusdCQiIqKkIFUXQ6o5gVqnj/2TiShusKDcRr5ACH9avgPfHa7BvKmFmDCEb6jao0+XNNx/3WBU13vxzJtbUd+GonLDsofh3fBqFNMRERHR6bQaESkWPWrqvQhVHoHkqlY6EhERUVKQ/W4IehMcLhaUiSh+sKDcBr5ACH96Zwf2HHHgtmn9cNFgFpM7om/XdNw/ewiqaj145q2tqHe3rqgsWtIh840sERFRTKXbDKhzeuFesRCBvV8oHYeIiCgpyH43ZL0JTncgvKYBEZHSWFBuJZ8/hP/793YUFTtwx/T+GDcoV+lIqlDYLR3/b/ZgVDg8ePbNrXC2oqgsWDM5M4qIiCjG0m0G1DQEAJ2RraeIKGmtXr0aU6dOxeTJk7F06dIW23fv3o1rr70WV111Fe666y7U19crkJLURPZ7EBAaC8mcoUxE8YIF5Vbw+oNY/O/t2HusFndO748xA3OUjqQq/bpn4L7Zg1Hu8ODZt7bB5Qmc8/GiNQOy2wFZCsUoIREREaXZDHDU+yDoTQALykSUhMrLy7F48WK88cYbWLlyJd5++20cOHCg2WOeeOIJ3HfffXjvvfdQUFCAv//97wqlJdXwe+CFHgALykQUP1hQPg+PL4jFy7bjwPE6/GjGAIwewGJyNAzonoGfXjsIpdVuPPvm1nMWlQVrJiDLkN21sQtIRESU5DJsBrh9QUBnguz3KB2HiCjmNm7ciNGjRyMtLQ1msxlTpkzBmjVrmj1GkiQ0NDQAADweD4xGoxJRSUVMV/4MJfaxAIB0KwvKRBQfWFA+B7c3gMXLtuPgiXrcNXMARvXPVjqSqg0syMRPrx2EkuoGPPfWNjR4z1xUFq2ZEIw2yF5XjBMSERElr7STb2KDGra8IKLkVFFRAbvdHr6dlZWF8vLyZo9ZsGABHnnkEYwfPx4bN27EjTfeGOuYpDIaewEqgjYAQLqNH1AQUXzQKh0gXnn9QTz18iYcLq3Hj2cOwIjCLKUjJYVBPTJx76xB+PO7O/HsW9sw/8ahMBt1zR6jyR8I69znFUpIRESUnJous/VYOsNo5p+QRJR8JEmCIAjh27IsN7vt9Xrx8MMP45///CcGDx6MV155Bb/85S/x8ssvt/oYmZnWiGa2220R3Z9S1DIOoG1jCXlcqN34LnSubBj1GnTNT2v2M6cktbwmahkHoJ6xqGUcgLrG8n18N3AWJyobcKS0Hj+eORAX9LWf/wkUMYN7dsI91wzCC+/uxHNvb8PPbxgGs/HUj2q8nECJiIiSSVNB+Uj+dORyPQkiSkI5OTnYsmVL+HZlZSWysk5NPNq3bx8MBgMGDx4MALjhhhvwf//3f206RnW1C5IkRySv3W5DZaUzIvtSklrGAbR9LCFHCdz/WwVfyjSkWrugqio+rtJVy2uilnEA6hmLWsYBJP5YRFE454ecbHlxFj3zUvHWb6eymKyQob064Z5rBqK43IU/LNsGjy/YbLt79ZPwfbVMoXRERETJp6mg7HD5IMuRKXYQESWSsWPHYtOmTaipqYHH48HatWsxYcKE8PZu3bqhrKwMhw4dAgB88sknGDRokFJxSQ1OtphyeEVkcEE+IoojLCifg1bDb4+ShvW24+6rB+JombNFUVn2uxFynFAwHRERUXIx6rUwGTTIOvoxXP/4EYvKRJR0srOz8cADD2Du3Lm4+uqrMX36dAwePBh33nkndu7cidTUVDz55JO4//77MWPGDCxfvhy/+93vlI5NCaxpzYIqtxBey4CIKB6w5QXFteF97PjxzAH4y8rdWPzv7Xji7nEAAMGSCdlVrXA6IiKi5JJuM8IVABAKNP6n1SsdiYgopmbMmIEZM2Y0u2/JkiXhrydOnIiJEyfGOhaplOz3AAAq3cBQzlAmojjCKbgU9y7om4UfzxyAQyfq8dt/fA1ZliFaMyGxoExERBRT6VY96vwaAKdmTREREVF0yL7Gc21DSB9uPUVEFA9YUKaEMKIwC9de3AM7D1ahwuGBYM0E/O7wJ7ZEREQUfWk2A2q8jYvjsqBMREQUXZqsHmjoNx0NsoEFZSKKKywoU8IYWJAJADhUWg/RmgEAkFw1SkYiIiJKKuk2I6o9J/985Ie6REREUaXp1A2lORMRhIYFZSKKKywoU8Lo3MkMg16DwyX10HYdAstNf4CYlqt0LCIioqSRbjPALTf2TeZVQkRERNEVqjqK0Ik9AMCCMhHFFRaUKWFoRBG98tNwuLQegt4E0ZoBQeSPMBERUaykWw04GuyEiql/gDZ/oNJxiIjazeFwKB2B6Lz8O9agy+EVEAUBKWYuhEtE8YPVOEoovbuk4Wi5C8GQBO9//4FA0edKRyIiIkoa6TYDZIiobQgqHYWIqEOmTZuGn//859iyZYvSUYjOSva74YMOqVY9RFFQOg4RURgLypRQ+nRNRzAk4XilC8ETuxEsLVI6EhERUdJItxmgQQjZ376EwP6NSschImq39evXY+zYsXj66acxY8YMLF26FC6XS+lYRM35PXBLemSw3QURxRkWlCmh9OmaDgA4XFIP0ZoJ2VWtcCIiIqLkYTXrAFGDFOchSLWlSschImo3o9GIa6+9FsuWLcMjjzyCf/zjH7jooovw+OOPsx0GxQ3Z70ZDSIs0FpSJKM6woEwJJSvdBJtZh0Ol9RCsGZBcNUpHIiIiShqiICDdZoRfMED2u5WOQ0TUIZ9//jl++tOf4oEHHsCkSZPw1ltvITc3F/fcc4/S0YgANC6AWx/QIt3KgjIRxRet0gGI2kIQBBTkpuBwqRPi4EwED22GLEsQBH42QkREFAtpNgN8Hj0sfo/SUYiI2u2SSy5BWloabrrpJjzzzDMwGo0AgL59++Ltt99WOB3RSVm9cbhcQm4KC8pEFF+iWoVzuVyYPn06jh8/3mLbnj17MGvWLEyZMgUPP/wwgsHGxV1KSkpw880344orrsDdd9+NhoaGaEakBNQjNwWlVQ0IGtIAKQTZU690JCIioqSRbjXALekg+zhDmYgS13PPPYfXX38d1113HURRRHX1qVZ6n3zyiYLJiE6pH/oDfOEr5AxlIoo7USsob9++HXPmzMGRI0fOuH3+/Pn49a9/jY8++giyLGPZsmUAgMcffxw33XQT1qxZg4EDB+LFF1+MVkRKUAWdUyADOGHoAdOU+yHoTUpHIiIiShrpNgMaQlrInKFMRAmsrKwM11xzDQDgxIkTmDZtGtavX69wKqJTZElCrcMBARLS2UOZiOJM1ArKy5Ytw2OPPYasrKwW206cOAGv14uhQ4cCAGbNmoU1a9YgEAhg8+bNmDJlSrP7iU5XkJsCADhQq4W221AIWp5ciYiIYiXdZsC7DSMgXXCD0lGIiNrtr3/9K1599VUAQEFBAVasWIHnn39e4VREp8gNNejy6SMYqT/IgjIRxZ2o9VB+4oknzrqtoqICdrs9fNtut6O8vBwOhwNWqxVarbbZ/USns5p0yEoz4ciJWvh3FkHM7Apt535KxyIiIkoK6TYDjocyUafLgk3pMERE7SRJEnJycsK3c3NzIUmSgomImmu6Esgr65HGlhdEFGcUWZRPkiQIghC+LcsyBEEI//9037/dGpmZ1g5nbGK3q+etklrGYrfbUFiQge8OVcPvXAnbkEvQachIpWO1i5peE7VQy1jUMg5AXWMhUoN0mwF9tKWQdrwHTLpJ6ThERO2SkZGBt956C7Nnz4YgCFixYgU6deqkdCyiMNnfuFaBrDdDr9MonIaIqDlFCso5OTmorKwM366qqkJWVhYyMjLgdDoRCoWg0WhQWVl5xpYZ51Nd7YIkyR3OabfbUFnp7PB+4oFaxtI0js4ZZny+9QSkjHS4K8sScmxqe03UQC1jUcs4gMQfiygKEf2QkygepFsN6KkrR9qhnZDlGyEIUV3jmYgoKhYtWoSf/exnWLRoEQRBwIABA/Dss88qHYvolJMFZZ3JonAQIqKWFCko5+XlwWAw4JtvvsEFF1yAVatWYcKECdDpdBgxYgQ+/PBDzJgxAytXrsSECROUiEhxrsfJPspuTQpsrurzPJqIiIgiJc1mgFfWQYAMBHwAF8clogTUvXt3vPvuu6irq4NGo4HVyg+AKb40tbwwWvizSUTxJ6YF5TvvvBP33XcfBg0ahGeffRaPPPIIXC4XBgwYgLlz5wIAHnvsMSxYsAB/+ctfkJubiz/84Q+xjEgJomu2FaIgoCZkhtV9Quk4RERR8dxzz+HnP/+50jGImtFqRMi6xiKy7HdDYEGZiBJQTU0N3nvvPTQ0NECWZUiShKNHj+K5555TOhpRIykEr6yD2cb2b0QUf1pVUK6qqsL27dtx2WWX4ZlnnsGuXbvwq1/9CoWFhed97vr168NfL1myJPx1YWEh3nnnnRaPz8vLw2uvvdaaWJTE9DoN8rMsKPUa0MXnhBz0Q9DqlY5FRBRRn332GQvKFJc0RgsQaurvmKl0HCKiNrv//vthNBpx4MABjB07Fhs3bsQFF1ygdCyiMKHXOCxYEcBVAzKUjkJE1EKrmt4tWLAAx44dw6ZNm7BhwwbMnDkTv/3tb6OdjeiceuSmYHNtJnQXXANIIaXjEBFFXH5+Pm677Tb8+c9/xiuvvBL+j0hpBnPj5bdNl+MSESWakpISvPzyy5gwYQJuueUWvPnmmzh06JDSsYjCal0+yGhcDJeIKN60qqBcW1uLefPm4fPPP8f06dMxa9YseDx8A0HKKshNwT5PBhzdJ/FyWyJSpbS0NGRnZ+PEiRPYt29f+D8ipckp2VgXGA7RnK50FCKidunUqROAxl7K+/btQ3Z2NoLBoMKpiE4JfLMCP7Z9zIIyEcWlVrW8CAQCCAQC2LBhA37/+9/D4/HA7XZHOxvRORV0ToEICRVF25A9sBfE1GylIxERRdSTTz4JADhx4gSCwSC6deumcCKiRsY0O1Y4B2KaKQNsOEVEiSgzMxN/+9vfMHToUDz//POwWq3wer1KxyIKk+rKkCE2wGhlQZmI4k+rZihfdtllGDNmDNLT0zFw4EBcd911mD59erSzEZ1T50wLjDoRBbuXILB/o9JxiIgi7ujRo5g2bRquvvpqzJo1C5MmTcLBgweVjkWEdKsOfbSlqCvjwrhElJgWLVoEvV6PESNGYODAgfjTn/6EX/ziF0rHIgqTvG54ZD3SOEOZiOJQq2Yo33fffbj++uuRnd04A/TZZ59t1YJ8RNEkigK65KShocECnatG6ThERBG3aNEi3HHHHbjmmmsAAMuXL8fjjz+OV199VeFklOzSLTr8JOVj1B/QAV26Kh2HiKjNnnrqKTz99NMAgPnz52P+/PkKJyJqTva74YceFmOryjZERDHVqhnKVVVV2L17NwRBwDPPPIMnn3wSRUVF0c5GdF4FnVNQHTRBclYpHYWIKOKqq6vDxWQAuPbaa+FwOBRMRNQoLc2KgCzC73YpHYWIqF327NkDWZaVjkF0VmLQi6DGCEEQlI5CRNRCqz7qWrBgAcaPH49NmzZhw4YNmDdvHn7729/i9ddfj3Y+onPqkZuCml0W5NezoExE6hMKhVBbW4u0tDQAQE0Nr8ag+JBhM8Ah6xHwNCgdhYioXbKysjBt2jQMGTIEFoslfP8jjzyiYCqiU7QhL6Dj4vNEFJ9aVVCura3FvHnz8NRTT2H69OmYNWsWli5dGu1sROdVkJuCLyQLBPcJyLLMT2+JSFVuueUW3HDDDbjyyishCAI+/PBD3HrrrUrHIoLJoEUp9JB8XKSZiBLTsGHDMGzYMKVjEJ3VP0MzkJ+VhlFKByEiOoNWFZQDgQACgQA2bNiA3//+9/B4PHC7+QaClJeRYkClNgcnDEGkhPyAlgsWEJF6zJo1C926dcOGDRsgSRIee+wxjB07VulYRACAgGgA/B6lYxARtcu9996rdASis5JlGYdcRvRMtSsdhYjojFpVUL7sssswZswY9OvXDwMHDsT06dMxffr0aGcjOi9BEODOHoqljj54gsVkIlKZ2bNnY9WqVRg9erTSUYhaqNTlIygBXKaZiBLRjBkzznj/6tWrY5yEqCVnXR1mGTYiVbgYQC+F0xARtdSqgvJ9992H66+/Hjk5OQCAZ599FoWFfPtA8aEg14aigyVocLpgsVmVjkNEFDEmkwllZWXh8y9RPCmyX46iYgcuVjoIEVE7PProo+GvA4EAPvjgA3Tp0kXBRESn1FfXYJxxP0rF4UpHISI6o1YVlCVJwurVq/H5558jGAxi3Lhx6NWrF7TaVj2dKKp6dhLxVPpbqP7WC8vEq5WOQ0QUMR6PB5dddhlycnJgNpvD93P2FMWDdKseHmcDJEmGKHINAyJKLCNHjmx2e+zYsbjxxhtx9913K5SI6BRnXR1SAZhsKUpHISI6o1ZVhJ977jkUFRXh1ltvhSRJePvtt/H000/joYceinY+ovPq0iUXflkDV3W50lGIiCJq/vz50Ov1SscgOqPBdZ9iYtpXqHdPRJqVbaeIKLE5HA5UVFQoHYMIAOB2OgEAVhaUiShOtaqgvGHDBixfvhw6nQ4AcPHFF+Oqq65iQZnigs2sxxHYEKyvVjoKEVFEPfPMM1i1apXSMYjOSGeywCAEUVXnZkGZiBLO93sol5SU4IYbblAoDVFzHtfJgnKqTeEkRERn1qqCsizL4WIyAOj1+ma3iZQWMKRC63UoHYOIKKLYQ5nimcHS+Ca3rrYOyEtXOA0RUduc3kNZEARkZGSgZ8+eCiYiOsXvdgEANEauEURE8UlszYMKCwvxu9/9DsXFxTh27BiefPJJ9OnTJ9rZiFpNtGUiBS44nD6loxARRUxTD+XLLrsMM2bMCP9HFA/MtsaCsquuTuEkRERt17VrV3z44YcYOXIkMjMz8dxzz6GqqkrpWEQAgEMBO9ZrJ0AwseUFEcWnVs1Qfuyxx/Db3/4WN954I2RZxrhx4zBnzpxoZyNqNXOnzvBU7Ef5iVpcUJitdBwiooh4+OGHlY5AdFYmmw0+nOrzSESUSBYsWIBLL70UAJCXl4eRI0fiV7/6FZYsWaJwMiLgqNsCb8aFELRcS4OI4lOrCspWqxW///3vm903fPhwfPvtt1EJRdRWncZdg3u+ysSUMhcLykSU8EpKStC5c+cWK9ADwOeff65AIqKWRIMFflmLBpdb6ShERG3mcDgwd+5cAIDBYMC8efOwcuVKZUMRnWRrOIZ+mQYAg5SOQkR0Rq1qeXEmsixHMgdRh+i0GuRnWXG4tF7pKEREHfaTn/wk/PVPf/rTZtsWL14c6zhEZ6TtXIi/mn+MA3670lGIiNosFAqhvLw8fLuqqorvcSku+PwhDBf3YHDtx0pHISI6q1bNUD4TQRAimYOoQ6QGB+4KvY73KodAkodC5M8nESWw09/QHjt27KzbiJSWbjXgeGWD0jGIiNps3rx5uPrqq3HRRRdBEARs3LgRDz74oNKxiOBw+WAS/IDOpHQUIqKzandBmSieCAYLLKE62KR6lFW70bmTRelIRETtdvqHtt//AJcf6FK8kANezKh/Ax95ekKWR/Fnk4gSyuzZszFw4ED873//g0ajwR133IHevXsrHYsIDqcPRiEAQW9WOgoR0Vmds6A8bNiwM745kGUZXq83aqGI2krQ6iEZbEj3NuBwaT0LykSU0DgLmRKCRo80XylS5Cx4fCGYjZynQESJo7y8HG+99RYWLlyIQ4cO4dlnn8Xjjz8Ou51tfEhZDqcXaYIfGmMnpaMQEZ3VOf/yf//992OVg6jDtLZMdGpwY09pPcYNylU6DhFRu0mShLq6OsiyjFAoFP4aaOz5SBQPBFFESGOASfTD4fKxoExECeWXv/wlLr30UgBAXl4eRo4ciYceeghLlixROBklO4fTh1zBD73ZqnQUIqKzOudf/nl5ebHKQdRhojUTdv1hfFjChfmIKLHt27cPo0ePDheRR40aFd7GtgIUV3QmGAU/HE4v8nh1EBElEIfDgblz5wIADAYD5s2bh5UrVyobighArdOPvVJX5OUVKh2FiOisOJWEVEOwZiBF3oljFU4EgiHotBqlIxERtUtRUZHSEYhaRdCbYBICcDh9SkchImqTUCiE8vJyZGdnAwCqqqrYcoriQo3TiyLDRFzRb9T5H0xEpBAWlEk1DMNnYk/qeIRWH0RxhQs9O6cqHYmIiEjVNEYLTIILJSwoE1GCmTdvHq6++mpcdNFFAIBNmzbhwQcfVDgVEVDn9KCrxQs56IOgNSgdh4jojESlAxBFimC0oluXxhkGh9n2goiIKOpME2/De6GLOEOZiBLO7Nmz8corr6B///4YNGgQbrjhBrz66qtKxyJCwFWLG52vILBvo9JRiIjOijOUSTUkVzWM/1uKwbbOOFzKgjIREVG0adI7A9bjLCgTUULKzc2F3+/H0qVL4Xa78YMf/EDpSJTkQpKEgKcB0De2lSIiilcsKJN6iBqEjnyL/mlp+LTUqXQaIiIi1Qsc+RaTNV/hYxf7PBJR4jh06BD+9a9/4b333kNeXh68Xi/Wr18Pm82mdDRKcnUuP4xCAAAgGMwKpyEiOju2vCDVEEwpgKhBnsmH8ho3GrwBpSN1iOx3I3BoMzyfvswFQoiIKC5JFQcx0LcVDqdX6ShERK3yox/9CLfccgt0Oh1effVVvP/++7BYLCwmU1xwuHwwCX4AgKBnQZmI4hcLyqQagiBCsGQgU+sGABxJ8FnK/u8+g3fdCwju3wjJcVzpOERERC3pTRAhwev2IhCUlE5DRHRe3333HQYMGIDevXujW7duAABBEBRORdTIUe+D8WRBGWx5QURxjAVlUhXRmglLqLGQfCjB+yiHTuwGDBYAQPDoNmXDEBERnUHT7CmT4Eedi32UiSj+ffbZZ7jmmmvw/vvvY/z48bjvvvvg8/HfL4oPjqZzqTkdwsn3gkRE8YgFZVIVwZoJwV2DnAwzDpckbkFZDvoRKtsLXe9xEO0FLCgTEVFcOr2gXMOF+YgoAWi1WkydOhWvvfYa3n33XWRlZcHn82Hy5Ml48803lY5HSa7W6cPOUE9Yb/4DRHOa0nGIiM6KBWVSFf3gKTBeehcKclNwqLQ+YXsPh8r2AaEgtPkDoO06FFLFIUiexC2QExGROjWtQG8UAqjlDGUiSjC9evXCI488gs8//xy33347li1bpnQkSnIOpw9pVj3bsBBR3GNBmVRFk9kV2ty+6NE5BfUNfjgSdLZU8PhuQNRAk1sIbbehAGSEircrHYuIiKgZMSMfwoU3olayJOw5l4jIZDLhhhtuwIoVK5SOQknO4fRhqmELGlY8rnQUIqJzYkGZVEVqcMD3zSr0sjWuNn8oQdteCKIG2q5DIegMEDO7wjTjV9D2HqN0LCIiomZEayYsQ6fAo7GyoExERNRBDpcPGVoPZF+D0lGIiM5Jq3QAokiS/W74v1mB7Il2aEQBh0vrMaIwS+lYbWYYOTv8tSAI0Ob2VTANERHRmcmhIILF29DT5mFBmYiIqANkWYbD6YPFEgi3lCIiilecoUyqIloyAACCpwZds604XJp4M5Qldy3kYPM35ZKrGu41ixE88Z1CqYiIiM5ACsL78Z8xxHD81Mr0RERE1GYN3iACQQlGIRBe9JaIKF6xoEyqIuhNgMEC2VWDgtwUHC5zQpISa2E+39fL0fDmg80WFBSMNoRO7EHwyDcKJiMiIvoerQEQRKToQnDUs6BMRETUXrUnr/TRyz4WlIko7rGgTKojWjMguapRkJsCnz+E0urE6T8lyzJCJ3ZDk9O72cq+glYPTV5/BI9ua1ZoVhupwQHfV8vYM4yoA1avXo2pU6di8uTJWLp0aYvt69atw8yZM3HVVVfhnnvuQV1dnQIpSS0EQQD0Jli1QdS6fJBUfI4iIiKKppqTBWWd5AXY8oKI4hwLyqQ6giUTsqsGPTqnAAAOJVDbC6muFHJDDTR5A1ps03YbCtlVDclxQoFksRHYvQ7+7R8CGp3SUYgSUnl5ORYvXow33ngDK1euxNtvv40DBw6Et7tcLixcuBAvv/wy3nvvPfTt2xfPP/+8golJDQS9GRZNACFJhssdUDoOERFRQqo92ToqNOUhGEZdr3AaIqJzY0GZVEfXdzx0Ay5DdoYZJoMGh0udSkdqtdDx3QAAbf4ZCspdhwAAgke3xTJSTAWP74Impw8ErR6hysMI7PtS6UhECWXjxo0YPXo00tLSYDabMWXKFKxZsya8PRAI4LHHHkN2djYAoG/fvigtLVUqLqmEoDfDCD8AcGE+IiKidqqp90IAkNqpE0RTitJxiIjOiQVlUh1dwQjo+10MURDQPScFh0sSZ4Zy8PhuCDY7xJSsFttESzrETt0RKlHnwnySpx5S1VFo8gcCAPy7Pob3syXwblwKWQoqnI4oMVRUVMBut4dvZ2Vloby8PHw7PT0dl19+OQDA6/Xi5ZdfxqRJk2Kek9RF23UwBHsPACwoExERtVety4dMCxD49K8InpxoREQUr7RKByCKNNnrQqhsP8ScXujROQVrviqGPxCCXqdROtp5iSYbNOkXnnW76fKfQDCnxzBR7ISO7wIAaLsMBgAYJ94On9GGwM6PIFUXwzjpJ/yknug8JElq1n9dluVmt5s4nU785Cc/QWFhIa655po2Hycz09qhnKez220R25eS1DIOoB1jmToPpjoPsGktAu15fpTES45IUMtY1DIOQD1jUcs4iNTA4fQj1yojeOhraE9OsiEiilcsKJPqSLWl8Kz9P5iu/BkKcnMRkmQUV7jQKy9V6WjnZZx4+zm3izb7ObcnsuCxnRCMNoidugIABFED45g50HTqBu/nr8D97kKYJv8UGnuBwkmJ4ldOTg62bNkSvl1ZWYmsrOZXPFRUVOD222/H6NGj8dBDD7XrONXVLkhSxxdfs9ttqKxMnLZEZ6OWcQDtG4sc8CHodUEQgGOldXHxvUj21yQeqWUcgHrGooZxiKIQ0Q85iZTkcHrR2wIgAMBgVjoOEdE5RbXlxblWmt+zZw9mzpwZ/u+iiy7C9OnTAQArVqzA+PHjw9sWL14czZikMoI1AwAguWpQkNs4ozUR2l5I9RWQg+e/VNi74V/wfPpyDBLFln7g5TCM/wEEofk/S7reY2Ge+TAgCJCcVQqlI0oMY8eOxaZNm1BTUwOPx4O1a9diwoQJ4e2hUAg//vGPceWVV+Lhhx8+4+xlorbybX4H3nceQZrVwJYXRERE7eRw+pBpkgA0rk9ARBTPojZDuWml+XfffRd6vR433ngjRo0ahV69egEA+vXrh1WrVgEAPB4PrrvuOixcuBAAsGvXLixYsCBcYCZqC8GcBggiZFc10m0GpNsMOFwa/wVlz6cvQ4DQWDw9JxnBI99CDgUgaHQxyRYLmqwe0GT1OPO2Tt1huf5JCFo9ACBw8CtoCy6AIPIiC6LTZWdn44EHHsDcuXMRCAQwe/ZsDB48GHfeeSfuu+8+lJWV4bvvvkMoFMJHH30EABg4cCCeeOIJhZNTIhP0ZiDgRZpFz4IyERFRO/gDITR4g0g3hACwoExE8S9q1ZjTV5oHEF5p/t57723x2JdeegkXXnghRowYAQDYuXMnjhw5gpdeegl9+/bFo48+itTU+G9XQPFBEDUQLOmQXDUAgILcFByK84Ky7HdDqjgE/dBp532stutQBPZ8hlDpXtX01goc/Bqypw66AZPOOmOyqZgcKj8A7yd/gSa3L/sqE53BjBkzMGPGjGb3LVmyBAAwaNAgFBUVKRGLVEzQmwDIyLIKKHawoExERNRWDlfj+TNF31RQNikZh4jovKLW8uJ8K803cTqdWLZsWbNCs91uxz333IP33nsPubm5WLRoUbRikkqJ1kzIrmoAQEGuDRUOD1yegMKpzi5YsgeQJWjyBpz3sZq8/oBGj+DRrTFIFhuB3esQ2PtFqy6/12T3gvGSHyFUcQjudxciVHk4BgmJiOhsmmZR2c0yal0sKBMREbVV7ckrfDQ5fWC85EcQLGnKBiIiOo+ozVBu7Urz7733HiZNmoTMzMzwfS+88EL46zvuuAOXX355m47N1efPTC1jac04HIUjIPncyLTbMKxfDpb/9xAc7iAKumbEIGHrNY2l6pv98OmMyBk4tFVtLKQeg+E/vgOdOlnjogdqR362JG8DnBUHkTZ6JjJaux/7FPh69Eb5O0/D897v0GnqXbANvqTdGZrtOol+TxKFmsZCpEonZ1FlmCR4fCF4fEGYDGxJRERE1FpNLaNSsztDl9lb4TREROcXtb/2W7PSPACsW7cOd911V/i20+nE8uXLMW/ePACNhWiNRtOmY3P1+ZbUMpZWj6PPZABAZaUTaUYtBABbi8rQJTN+Lh06fSyuA1sh5vRBVY0XgPe8zw3lDETw8E5UHDkK0Zp53sdHU0d/tgKHvwGkEHwZfdq2H40dhpm/hnfdi6je8C48WUM63FM66X5PEkCij4Wrz1MyEIxWCOY0pJoa/16rdflYUCYiImqDcEHZdQSBagd0fcYrnIiI6Nyi1vLifCvNA43F4t27d2PYsGHh+8xmM/72t79h+/btAIDXX3+9zTOUiWRJguSqhhwKwGzUIifTjMMl8dlHWQ76ocnoAm3BBa1+jq73WFjnPq94MTkSQsd3AjojNNm92vxc0WiDaeovYJr+IASNDpKrGpK7LgopiYjobLSd+8F6yx9hyu0JAFyYj4iIqI0cTh+Meg3Eo1/D99W/lY5DRHReUSson77S/NVXX43p06eHV5rfuXMnAKCmpgY6nQ4GgyH8PI1Ggz/+8Y9YuHAhrrzySuzevRvz58+PVkxSqdDxHWh44+eQqosBAD1yU3C4tB6y3PGZ65EmaPUwXX4v9IUT2/QcQauHLIWimCz6ZFlG8NhOaDv3g6Bp32w2QdRANKdBlmV4178E94rHEao4FOGkRER0Pum2xr/nWFAmIiJqG4fLh3SbAbLfDcFgVjoOEdF5RfV6xHOtNA8AmZmZ+PLLL1s8b8SIEVixYkU0o5HKCSdn7kquamiyeqKgcwq+3FWG6novOqXGT9sLAAhVH4OYmg1Bq2/T8wIHv4J3wz9hueEpiKaUKKWLPuMlP2p3Mfl0giDAMPZmeNb+Ce7Vv4Nx/K3Q9b0oAgmJiOhcZF8DGlYugm3QVAAsKBMREbWVw9lUUPaE1yYgIopnUZuhTKQk0dK4+J7sqgYAFOQ2FlwPl8ZXL1ZZluB5/yl4v3i1zc8VU7IBvwehYzuikCw2BEGANrcvNFk9I7I/TaduMM9aCE1OH3j/+3d4v3wNshSMyL6JiOgstHrIdeXQeOtgMWrhcLGgTERE1BbhgrLPDUHPGcpEFP9YUCZ10psBnRGSqwYA0CXLCq1GiLs+ylJVMWSfC9q8/m1+rtipGwRzGoJHt0U+WIz4tqxA4MCmiO5TNNpguvLn0A2+AoHdnyB4aHNE909ERM0JGh2g0UH2u5FmM8BRz4IyERFRa0mSjDqXv7F1lJ8FZSJKDFyCm1RJEASI1szwDGWtRkTXbBsOlcZXQTl4YhcAQNOOgrIgCNB2HYrAwf9BDgUj0jYiluSgH/7t/4GucAJ0vcZEdN+CqIFx9I3Qdh8OTXZvAIBv6/vQ5PaBNqdPRI9FRESAoDcBfg/SbQbOUCYiVVu9ejX+8pe/IBgM4tZbb8XNN98c3rZnzx4sWLAgfLumpgapqal4//33lYhKCaKuwQ9JlpFuNUDbcxTEFLvSkYiIziuxKlBEbSBm5AMQwrcLclOwYUcJQpIEjRgfk/NDx3dDzMiHaE5r1/O13YYgUPQZQqVF0OYPjGy4KAuV7QNCfmi7DIraMZqKx7LfjcDOj+Df/A7E7F4wDJkGTbchEIT4+DkgIkp4ejNkvwfpVgOOlbuUTkNEFBXl5eVYvHgx3n33Xej1etx4440YNWoUevXqBQDo168fVq1aBQDweDy47rrrsHDhQgUTUyKoPflBbLrNCEPvaxROQ0TUOqymkGqZLrsbpst+HL7dIzcF/oCE0iq3gqlOkQI+hMr2Q5M3oN370OT1h2CwQnJWRTBZbASP7cT/b+++46Oq0v+Bf+6dOzVtJsmkQgo1oVeJgAgqRSRgQcXuquy67q6ru6tr259rXVf9rq6Ku67bXMWCBQELYm+g0mtCh5BCEtLL9Ht+fwRGQgKkzOTOTD7v18uXzNxynpObzJl55tznQFagS80JeluSwYKoK5+EceLVEM21cKz6K5rfvA+ePd8FvW0iot5AMlgg3M2wxRhR3+SG16dqHRIRUcCtXr0aeXl5sFqtsFgsmDlzJlauXNnuvi+88ALGjx+PcePG9XCUFG6OLWZri9bDd+QghKtJ44iIiE6PCWXqNbLTWhbmC5WyF6qjEUrGSCiZo7p8DkkxIuqav8KQOzVgcfUUX/E26FIHQ9Ibe6Q9STHCMOw8RF3+Z5jOuRnQ6eA7chAAILyulhWVqdcSbgfc2z6Ga/27WodCFJbM038B07SftiwoBKCu0a11SEREAVdRUQG7/cdyBElJSSgvL2+zX0NDA5YsWYJf/vKXPRkehaljCWWrwYPmd+4P+BozRETBwJIXFLG8BzfC8cU/ETXvPsjWVCTZzLAYFewvq8eUkWlahwclNgHmGb/q9nkkWQfh8wKqF5LeFIDIgk9trIJaUwLjoMk93rYk66AfkAel/wTA5wEAeHZ8BteG5VDGzYLa7+wulyCh8KM2VMK97RN4Cr8CPA7o0nIhhMpyKESdJEcnAABsMS2J5JpGFxLiwmNMIiLqKFVVIUk/ltQTQrR6fMzy5ctx3nnnISEhodNtJCREdyvGE9ntMQE9n1YipR9A2764fAKKTkLfRCNKAMQmxCMmDPobKdckUvoBRE5fIqUfQGT15URMKFPk0psBVxPUxmrI1lTIkoTs1BjsLw2NGcrO4kIInR2SYujWeYS7GY2v/haGURfAOGpOgKILLskSB/OcuyDHJWsXgyQBR3/2urQhUCr2oXbNMuD7FdAPnAzDyFmQ41I0i6+r3Ns+weGKAmB4PnT2LK3DCVlCCDg/ewHefd8DkKD0Gw/D8BnQJfXXOjSisOTZvRq+8j2wHq39eGy2FRFRJElJScG6dev8jysrK5GUlNRmv08++QQ/+9nPutRGVVUjVFV0Ocbj2e0xqKxsCMi5tBQp/QDa70tJeQPiooyoKa8EADS6ZDhDvL+Rck0ipR9A5PQlUvoBhH9fZFk65ZecnIJFEevYbCnRWOV/LjstFsWVTXB5fFqFBQBQm+tQ+tK9cG9d1e1zSQYL5Ngk+A5uDkBkPUOSFShpOZCjbFqHAgDQJWbCfN4v0PfmZ6AfNBme3d+g6Y274asp0Tq0TvMWb0XznvVoXvoAHF+8CLWpRuuQWvGW7YRz9WIItefrqwqfF54930H4vJAkCZI5BoaRsxF1xZMwn/tzJpOJusFXVQTPzm8QH9syK5kJZSKKRBMnTsSaNWtQXV0Nh8OBVatWYcqUKa32EUJg+/btGD16tEZRUripaXC2lIw6VoLPYNY2ICKiDuAMZYpYUpQNgAT1+IRyaixUIVBU3oCBfayaxeYr2Q4AUPp0fUG+4ykZo+DeuByqswGyKbRvqRCqD87PXoA+dyqU9CFah9OKPj4VprOuh2HsRfDuXwvZ2lIaxfnd61DSh0DXZ3i7tzWGEsus2xEfBZR9tgTuravg3bcWxgmXwzD0XE3j8h7eBfe6pfCVFkC29YEkt3yf2bz8UUjRCdCl5UBJy4UUYw/4z1h1NsCz43N4dnwG0VwL0/RfQp89DqaJVwW0HaLeTDJYAJ8bFgOg6GTUMqFMRBEoOTkZt99+O6699lp4PB7Mnz8fI0aMwMKFC3Hrrbdi+PDhqK6uhl6vh9HYM+uEUPiraXQjIykawt2yeLxksGgcERHR6TGhTBFL0imQLHFQG6v9z2WntizMt7+0XtOEsrdkO2RzNOSEzICcT8kcBfeGZfAVbYE8aFJAzhksauV+ePf9ACU7dFe8li1xMAw9D0BLMtK793t4tqyEHN8XhpHnQ+mf50+IhhK19jCkuGToLLEwTrgM+typcH2/BJKp5TYV4XYAemOP1gf2le+Ba91S+Eq2QzLHwnjmFdDnTmuJx+eBZLHCV7Id3j1r4AL8yWXTlBu7/TNWaw/DveVDeHavBnwe6PoMg+HsG6DrMywAPSOi40nHZlN5nLDFGFDd4NQ2ICKiIMnPz0d+fn6r51588UX/vxMSEvDtt9/2dFgUpoQQqG1wYWT/BEg6b8vEC1Ng62gTEQUDE8oU0aToeIimHxPK1mgj4mON2FemXR1lIQR8JTtgyRoesKSknJgJyWKFt2gT9CGeUPYe2gpIUsjNTj4Z2RSDqAVPwLtnDdybP4Tz83/A0HAExjFztQ6tFeFsRNNb98EwOh+YdTUAQI5Ngnn6j6uLO795CWpdOYxnXgElZVBw4zm6SI1n7/dQq4pgzFsA/ZBpkJQfZ+tIOj3M590CIQTU2jL4SgvgKy2AaKz2/22ULv4jvAYrlLQc6NJyIUfHn6ZdFXA7IBmj4Ks+BM/u1dAPnAT9sOnQxacHtc9EvZl/NpWrGbYYE2coExERdYDD5YXL44M12gglcyCUzFFah0RE1CFMKFNEs8z6TZsaVNmpsdivYUJZrS2DaKqBOXskAvVxW5JkKNnjIJyhX/DdW7wVclL/sPrmXdIp0A8+C8qgSXB8+Bd4dnwGw6gLIMk6rUPz8+z9HlC9p3wTqvQdAVfZm3AsfxRKv/EwnnEZ5Fh7QOPwVeyDa/27UPqOgGHYeTCOvRDG8fMh6U9+26ckSdDZ0qCzpQHHleYQXjdkowXeAxvg3fV1y76xyVDScmCcfF2rL2SExwXPrm/g3vYxdAkZMJ93C5SsMYi66i8hXwaGKBIcSygLtwO2GCP2ldZpHBEREVHoO7bmQHwsS6QQUXhhQpkiWntJy36psVi/sxINzW7EWAw9H5Tqg5I1piWh7AncaU2Trg7cyYJEOBuhVuyHYew8rUPpEkmSYRg1B2rdYUAEZvXtQPHs+hZyfF/oEk9eRkU/cCKUrLFwb1kJ9+b34T2wEYaR58M4/pJut++rPADX+qXwFW0GjFFQMlsWopGMUV0+p6QYkDL/TlRU1EGtOgRfWSF8pYVQa8t+rMH8wZOQjFEtM9/dzZDt2VCyx7YcL+sgMZlM1CPkxEyYpi6EFB0PW7QXNQ1u/50KRERE1L6axpaEsjXaCOea1+A9sAHRVzyhcVRERKfHhDJFNG/xdrg3vw/zubf4k8v+OsplDRjRP6HHY9Il9IV5xq3QW2OAysDOKBaqD6KxCnJsUkDPGyjeku0ABJS+w7UOpcuUtBwgLUfrMFrx1ZRCrdwHY96C0+4r6Y0wjp0Hfc4UuNa+BeFr+VZDqL6W7Z2cdS3cDjg//we8BzcCxigYxl8Cw9DzfqynGgCSJEOXmNmSLB8+88e2vW5AkuErLYCSPgSG4TMhJw9gAotIA3J0vL+Gvy2mDl6fikaHR5svbomIiMJETX1LQtkWY4RwNgJC1TgiIqKOYUKZIprwOuEr2QG18Qh0RxPKmSkxkADsL6vv8YSyUL3wle2CLnlAUM7v/Ozv8FUeQNSCx0MyqaZkjYV5zu8hJ2ZrHUq3qI1VcG96H4aRsyHHJGodDry7vgEkGcqAMzt8jBxlg3nqQoijM609Oz6Dp+BLGM9cAKUDi9apDZWQY+yA3gTh88Aw7mIYhk0PaCL5dCTFAMv5v+mx9ojo5ITXDe+e7yAn9YMtpuW23ZoGFxPKREREp3D8DGWvu7lH30sTEXVHYFYEIwpRcnRLwlhtrPI/ZzYqSEuM0qSOsq98LxzvP95ye34Q6NKGQDRUQq0pDcr5u0vSKVDScgO2GKFmhArPjs/h2fm11pEAAGRbGvTDpkO2xHX62GNfPMgxdgivC44PnkTzh3+B7yS/Q77qQ3B8/ByaXrsTvsoDkCQJ5vN/C+OYuXwDTNSb+TxwfvVv+Iq3w3pcQpmIiIhOrrbBhRiLHnpFhnA7flzklogoxIV5Vofo1KSjCWXRWN3q+ezUWOwrrffPzuwpvuJtgCS1lE0IgmMLsnmLNgXl/N3hqy6G46O/wlcbmsnuzpBj7ND1HQbPzq/8pSK0pB80GaYzr+jWOZTMUYi67FEYJ1wO3+HdaH7rPji/fbmlrAQAX3UJHJ8sQvNbf4C3eDsMY+b6F/QLxdnwRNTD9C1fKAl3M+KPJZQbmVAmIiI6leoGF2zRLeOmcDe3WVCeiChUMaFMEU0yRgM6Q6sZygCQnRaLRocHR+qcPRqPt2Q7ZHu/bi1UdipylA1yQiZ8BzcF5fzd4S3aAu/BjZD0kfEmSZ8zFaKpBr5DWzSNw7PnO/hqSgJyLkmnh2Hk+Yha8Gfoc6dCrS4GdHp49n6P5rfug/fQVhhG5yP6iidgHHdR0H6PiSj8SLIM6M0Q7mbERhkgoWXWFREREZ1cbYPLXyqKM5SJKJywhjJFNEmSIEXHQ5yQUO7nX5ivHnZrzyQ4hasJauV+GEbnB7UdJXMU3BuXQ3U2QDbFBLWtzvAVb4Uc3xdylE3rUAJCyRwJyRwHd8EXUDJHaxKDcDvg/PLf0A+eDN3kawN2XtkcC9PkayFUHyRJgpI+FIbRc2AYPtO/uCUR0YkkgxnC7YCikxEbZUA1E8pERESnVNPoQr+0ls+mUZc+AoTA3Y9ERB3BGcoU8cxn3wjjuItbPZduj4Kik7GvtOfqKHtLCwAhoEsfGtR2lKzRLW04G4PaTmcIjxO+w7ug68Bib+FCkhXoc6bAV7y9ZUVmDXj2/QD43NAPmhSU80uyruX/pmgYx1/CZDIRnZJktADuZgAtq9VzhjIREdHJebwqGpo9/rUHJMXANUmIKGxwhjJFPF3KwDbPKToZmSnRPbown2SMhtJvPHTJ/YPaji4xC5bZvwtqG53lKy0EVB+UvsO1DiWg9MOmQz/kHM0Srd5d30KOS4Fs76dJ+0REx1P6neH/IGyLMaKi1qFxRERERKGr9uhaA7YYY8vi2KuehWHIuVCytLn7kYioMzhDmSKer2IvnN+9DuHztno+OzUWBw83wKeqPRKHkpYD83m/gCQH/3scoarwHt7Vps9a8ZZsBxRDu8n9cCabYyFH2SBUFUL0zO/RMWp9BXyHd0EZNImL4hFRSDCOmQvDsOkAAGuMETX1nKFMRER0MjUNxyWUXc3wFW+D6qjTOCoioo5hQpkinlpTCs+WlRBNNa2e75caC7dXRXFFU/BjcNTDW7IDwucJelsA4Du0GY7lj8JXtrNH2jsd44TLYJl3HySdXutQAs5XW4qm137X44vzeXZ9C0CCfuDEHm2XiOhkVGcD1NoyAEB8jBHNLi9cHtaCJCIiao8/oRxthDhaMoolL4goXDChTBFPik4AAKgnLMw3qK8VAFBYVHPiIQHnPbABjvcfh1pfGfS2AECXNgTQKfAWbeqR9k5H0umhS8jQOoygkGOTANULT8GXPdqufvBkmKbeCPno7zcRkdbc65aiedkjAABrdEs9SNZRJiIiat+PM5RNgOtYQtmiZUhERB3GhDJFPDk6HgAgTkgox8eakBxvQcHB4CeUfcXbIEXZIFtTg94WAEh6I3RpQ+A9uAlCiB5p82Q8O7+G46O/QngiM6kgyQr0g6fAW7QJalPwf5eOkWPs0A+a3GPtERGdjmSwQLgdEELAdnSBoWomlImIiNpV2+iCUa+D2aiDcLesO8AZykQULphQpognRbUklE+coQwAQzJt2HmoFl5f8OrfClWFt7QAuvShPVrrVskcBdFQCbW2tMfabI9n/3r4qosBxaBpHMGkz5kCCAHPzq96pD3XuqVwb/2oR9oiIuowgxkQPsDr9ieUOUOZiIiofdUNLlhjjJAkyV/yApyhTERhgglliniSYoBkjoVorG6zLTfTBpfbh/1l9UFrXz1yAHA1QekzNGhttEfJGAUA8B7c3KPtHk/4vPCVFkDpOzyiF46TY5OgSx8KT+FXEEFe5FF4XHBv/Qi+quKgtkNE1FnHbtMV7mZ/QrmmkQllIiKi9tQ2uBB/dLzUpeXAPOs2lrMjorDBhDL1CoaxF0HpN67N8zmZNkgACg4Er1SBt2Q7AECX3rMJZTk6HvrcaZBj7T3a7vF85bsBrwtKn+GaxdBT9LlTIUfFQziD9+UEAHgPrAc8TugHTQpqO0REnXXsNl3hbobJoMBs1KGmngllIiKi9tQ0uPxrDsgWK5SMUZD0Ro2jIiLqGEXrAIh6gmHItHafjzbrkZEcgx0HazB3cnZQ2pZt6dAPmw7ZHBuU85+K6azrerzN4/kObQUkHXRpOZrG0ROU7HHQ9xsf9HY8u76FFJMIXeqgoLdFRNQZkiUOsjUNEC13athiTJyhTERE1A5VCNQ2uvx39HiLt0GtKYVh+AyNIyMi6hjOUKZeQa0rh2fPd+0uUJebZcPekjq43L6gtK3PGgPTxKuCcu7TEULAV74HviMHNWnfW1YIXcrAXrG4hCRJLT/vin1QHcGZpaw2VsFXsgP6gZMgSXz5JqLQoqTlIuqyR6GL7wsAsEUb/CvYExER0Y8amj3wqT8uYus9sAHujSs0joqIqOOYkaBewXtwI5yf/R04ttjBcYZk2uBTBXYX1wa8XbW2DN7SAgg1OMnq0xNwrHoW7k3va9K6Zc5dME1bqEnbWhCNR9D87oPwFHwRlPN7D20FIFjugojCQlK8BaVVTfAFubY8ERFRuDm2aO2xhLJwNXNBPiIKK0woU68gHV3cQG2sarNtYB8rdLKEHQcDX0fZXfAFHB/+H6BRQlmSZCiZI+E9tBXC5+359hVDr1pYQo6xQ5c+BJ7CLyFE4BMohtypiLr8McixSQE/NxFRd6mOejS+fCvchV8CAAb3tcLl9uHA4QaNIyMiIgot1Q1OAMcllN3NveKuTiKKHEwoU69wLKkpGqvbbDMadOifHheUhfl8JduhSxkESTEE/NwdpWSMBjwO+A7v6tF2nV//F86v/9ujbYYCfc5UiMYq+Iq3B/S8Qm35QkCOSwnoeYmIAkVSjBCOeghnEwAgJ8MGACgMwhe2RERE4ezEGcpwO5hQJqKwwoQy9QpSdDyA9mcoAy1lL4rKG9Do8ASsTbW5Fmp1MXTpQwN2zq7QpQ8BdAq8Bzf1WJtCVeHdtw7CG7ifZ7hQssZAMsUEvOyFa/WraF7+aLt1wImIQoJiACSdv7xUbJQB6YlRKCyq1TYuIiKiEFPT6IIsSYi1tEw8apmhzJIXRBQ+mFCmXkEyxwKyDuIkCeXcLBsEAjuLyleyAwCg9NE2oSzpjdClDYG3aFOPJSPVIwcgXI1Q+g7vkfZCiaRToAyaDO/BTVCbawNyTuHzwLP3e0hR8ZAkKSDnJCIKNEmSIBnMEMetVzA4w4rdxbXw+lhHmYiI6Jiaehfiog2Q5Zb39vqcKVD6jdc4KiKijmNCmXoFSZKhH3QWZFtau9uzU2NhNOhQEMCEsrd4GyRTDOSEjICds6sMQ86BYcg5QBDq+rbHW7wVgASdxsl0rRhyp8J4xvyAlTrxHtwEuJq4GB8RhT6DGcLt8D/MybDB7VFxoIx1lImIiI6paXQh/li5CwCG4TOhH5CnYURERJ2jaB0AUU8xTbn+pNsUnYzBfa0BXZhPSR8KXXxfSJL239somaN6tD3voa2Q7VmQTTE92m6okOOSYRh5fsDO59n1DSSLVfPyKUREpyMZLW1mKANAQVENBvSJ0ygqIiKi0FLT4EJaYhSAlnKBvsM7IVtTIVus2gZGRNRB2me6iHqI8Lig1paddHtupg3l1c2orncGpD39oEkBTSp2l6+6GO4A1/Vtj/C6oVYX98pyF8cTbgdcP7wFb2lht86jNtfBd2gr9AMnQpL5kk1Eoc0y+w6Yz7vF/zjGYkAfezQX5iMiIjpOTYMLtuhjC/I1w/Hen+Hd+722QRERdQKzE9RruDe9h6Y374FQfe1uz81sWY0+EGUvfId3w1taGFILqHn3/QDXNy9BOBuD2o6kGBB9zTMwDJ8Z1HZCnk6Bp+ALeLZ/0q3TiIZKSNEJUAZNDlBgRETBI5miISnGVs/lZFixt6QOHm/o1VFWGyrhK9+jdRhERNSLOFxeON0+2GJbxstjpaK4KB8RhRMmlKnXkKITACEgTrJQWp+kaESb9dhxoPsJZdfGFXB+/d+QWkBNyRgFCAHvoS1BbUcIAUkxQDJGBbWdUCfp9FAGT4b3wEaozXVdPo8ueQCiFjwO3UnqfxMRhRL3js/h+PzFVs/lZNrg9qrYX1avUVStqY56OL97A0L1wvHJ83B89ncIr1vrsIiIqJeobXQBgH+Gsr9UlMGsVUhERJ3GhDL1GnJ0PABAbaxqf7skITfThoKD1d2aWSx8HvjKCqGEWL1b2Z4FyRwHT+FXQfvgLIRA89t/gHvzh0E5f7gx5JwNCB88u77p0vFqUw3UxqqQ+mKCiOhU1NpSeA+sb/Xc4AwrJCAkyl74qovR/O6D8Gz/BOqRIhgnXA7RcATuTe9rHRoREfUS1Q1HE8oxrRPKnKFMROGECWXqNaToBACAaKw+6T65WTbUNrpxuLr5pPucjq98D+B1Q9cntBLKkiTDMDofvrJCNK94DGpT4D/Yq7WlUKuL+e36UbI1FbrUwfAUfgkhOn+rt3vLSjS98Xv/bXBERKFOMlgAjxNC/fE1L8qkR9/kaBQWaZtQ9h7chOZlDwM+Lyz5d0OX1A9KWg6UAXlwb34fal25pvEREVHvUNsmocySF0QUfphQpl5Djjr1DGUAGHK0jnJ3yl74ircDkgwlLafL5wgWw7DzYJr+KwhnA9CFBOfp+A5tBYBevyDf8fQ5Z0PUV0A9crBTxwnVC++eNVD6joTEBD0RhQn/h2FP6y/CcjJs2FNSD4+3/XUMgkkIAffmD+H46K+Q41Jgueh+6JL6+bcbJ1wOyAqcqxeH1NoHREQUmWqOJpStR0teSHoTdCmDIJljtAyLiKhTmFCmXkMymCHH94Ukn/zX3m41IyHW1K2F+bwl2yEn9QvZb5j12WMRdfmfIEcnQLgd8Oz7IWDn9h7aCtmaBvnobHAClOxxiLr8Mejs2Z06zndoG4SjHnouxkdEYUQytox9/nqQR+Vk2OD1qdhbokEdZSHgLdkOpd84WObeDTnK1mqzHGWDcexF8JXsgKg73PPxERFRr1LT4EKUSYFBrwMAKOlDYJl7Dz9DEVFYUYJ58hUrVuBvf/sbvF4vrrvuOlx11VWttj/33HN4++23ERsbCwC47LLLcNVVV6G0tBR33HEHqqqqkJ2djSeffBJRUb17gS8KjKj5D51yuyRJyM2yYcPOSqiqgCx3vnatfuAkSKborobYIyS55U/fvfUjuNe/C9+w3TDmLYAk67p8TuF1wXd4J/RDzg1UmBFBUgyQ4lJaZr2pPki6jr3senZ9A8kUA10GZ3sTURg5ekfFiaV6BvW1QpKAwqIa5GTa2jsy4FRHPURzLXQJGTDP+BWg00OS2v9SWT/sXChZoyHHJvVIbERE1HvVNLhgizH5HwvVB0gy100horAStBnK5eXleOqpp/Dqq6/i3XffxRtvvIE9e/a02mfbtm34y1/+gmXLlmHZsmX+hPMDDzyAK6+8EitXrsSwYcPw/PPPBytM6oVOdzvrkEwbml1eHCxv6NL5DcPOg35AXpeO7WmG0fnQD5sBz7aP4fjgSQhnY5fPpVaXAACUPsMCFV7EEG4Hmt+8F55tqzq2v7MR3oOboAzI8yf/iYjCgS55AMwzft1mlpXFpCAzOabHFuZzlR9A89IH4Fj1LITqhaQYT5pMBlq+aJVjkyB8XnjLdvZIjERE1DvVNLr89ZMBwPXDm2j87y0aRkRE1HlBSyivXr0aeXl5sFqtsFgsmDlzJlauXNlqn23btuGFF15Afn4+HnzwQbhcLng8HqxduxYzZ84EAFx88cVtjiPqKte6pWh65bZT7pN7dOZUV8peeA9sbFmUL0xIsg6miVfCdPaN8B3ejaalD8BXdahL59Il9UP0dYugS88NcJThTzKYIZmi4S74skP1OYXqg37oudAPntID0RERBY5ssULJGg3J2PbOspwMG/aV1cPlCW4dZe/BjSj9372A6oP5vFs69cWca+3bcLz/BFSWviAioiBpmaFs+PEJVzMkvfHkBxARhaCgJZQrKipgt9v9j5OSklBe/uPq2U1NTcjNzcUdd9yBpUuXor6+Hs8//zxqamoQHR0NRWl582+321sdR9QtihHCUdfmVtzjxUUbkZ4YhYID1Z0+vXPNq3Bver87EWpCP/gsWObeDfg88BR+2aVzCFVtmQHGGbXt0udOhagvh6+s8LT7ypY4mM68ArqEvj0QGRFR4Ai3A+4tK+GrKmqzLSfTCq9PYG9JXXDaFgKuTR/A8dEzMCSktyy+18n69YYRMwEdF+gjIqLg8HhVNDS5W5e8cDeH7Po7REQnE7TMj6qqrWoACSFaPY6KisKLL77of3zDDTfgnnvuwZVXXtmmdlBnawklJASufq3dHjkrrUZKX7rTj8a0NFQAsBpcMNhPXidxTG4yPvruIKw2C/RKx+oKN+38Hg0NlYibeCHiOhhjSF0T+yh4M5+EzhwNSaeHu7II+sQ+p7xF+Bir3oGSf/0O9rm3ImrguB4INniCdU1U61QUrXkV8r5vYB95xkn381SXoXnvRsSMmArZ2PU3liH1u9VNkdQXokgnfB64vnsdxolXQ5eQ0WrbwD5WyJKEwqIaDMmKD3jb6pGDcP/wJpR+45E6/zZU1bo7fQ7ZYoVx3EVwrXkN3oMboM8aG/A4iYio96qpd0IArUpeCLfDvwYBEVG4CFpCOSUlBevWrfM/rqysRFLSjwm80tJSrF69GvPnzwfQknBWFAXx8fFoaGiAz+eDTqdrc1xHVFU1QlW7P6vEbo9BZWXX6uiGmkjpS3f74VVbbsGtOlQEBdaT7peVFAW3x4fvNpV0aPEgta4cTcuehWzPhqvPGR2KMTSviQI4nFAbS9C05B4o6UNgmvZTSKd4g2O3x6Bi83dQnU1oQAyaQ65PHRfsa6IbcCaadnyO8qISyObYdvdx/fAh3Js/hDNpBGRL124LD83fra4J977IshTQLzmJQt2xGVbC3dxmm9moICs1BoUHawPapnA3A3ozdPYsmOfeDV3yQMh6I4DOJ5QBQD/0PHh2fg3X6leh9BkGSeFtyEREFBhVdU4AgDX6+IRyc8gv6k5EdKKglbyYOHEi1qxZg+rqajgcDqxatQpTpvxYD9RkMuGJJ57AoUOHIITA4sWLMX36dOj1eowbNw4ffPABAODdd99tdRxRd8jRLTOi1MZTl7MY3NcGSQJ2dKCOsvC64fjkOUCWW2o16vQBiVVLUlQ8jGfMh7doM5qXPQy1vuKU+/uKt0GKToAcl9pDEYYnfc5UyHEpEE3t//4JVYVn92ro+g6HbInr4eiIiLpP0imAztBuQhkABmdYsb+sHi53YOoo+6qK0PTWH+DZ9jEAQEkZ1Ok7204kyToYJ10D0VgFz+41gQiTiIgIAFBV31J6Mf64GcrweSDpOUOZiMJL0BLKycnJuP3223HttdfiwgsvxJw5czBixAgsXLgQW7duRXx8PB588EH8/Oc/x6xZsyCEwE9+8hMAwP33348lS5Zg9uzZWLduHW677bZghUm9jGSxApIM0XTqRLHFpCA7NRYFB09fR1mtK4doroN52s8gx9hPu384kCQJhmHTYZ79O6jNtWha+gC8xdva3Vf4vPCWbIfSZ3i3P8RHOl18OizzH4IuMavd7b7SHRBNNdAPnNSzgRERBZBkMAMnSSjnZtjgUwV2l9R2ux3PgQ1oXvYIIFToUgZ1+3zHU1IHwzLvPuhzzg7oeYmIqHc7Unt0hvJxCeWo+Q/DdM7NWoVERNQlQV09Kz8/H/n5+a2eO75u8syZMzFz5sw2x6Wnp+Pll18OZmjUS0myDtHXPQd04Bvg3EwbPvyuCA6XF2bjyf9UdAl9EbXgiYhcmVdJH4Koi+6HY9UzcKx6FlFXPgnZ1LqerbNkF+BxQtd3mEZRhhdJkuCrLYUECbK19Yxuz65vAYMFSuYobYIjIgoAyWA+6eK3A/rEQSdLKDxYi2HZCV06vxAC7s3vw/3D25DtWTDPuBVy1OnLU3WWLnkAgJZZ0HJ8X35pSkRE3VZV54BekRFlav35UpKDNtePiCgo+KpFvY5ksHToQ+GQTBtUIbDzUG27231HDsDx6d8h3I6ITCYfI8cmwTLvPphn3QbZFAOh+iC8P9al9FSXAooRSvoQDaMMH8LnRfO7D8O1YVnr590OePevh77/GZAUg0bRERF1nz73bCgZI9vdZjK03AFUWHT6klIn417/Ltw/vAWl/xmw5N8dlGTyMd6iLWh++//Be2BD0NogIqLeo7rOCVuM0f95VPg8aHr3QXj2fKdxZEREnRPUGcpEoci9dRV8ZYUwz7j1lPsN6BMHvSKj4EANRg1IbLVNuJrg+Pg5QFUhVC8ifc6SpDdBScsFALi+ex2+8j3+GWGxo86DM2VMRNSO7gmSToF+4ER4Cr6AmNj44wIceiPMs3970sX6iIjChWHE+afcPjjD2qE7gE5GP3gyJL0R+hHnB33WsK7PUMjxfeBac3SBvgj+ApmIiILvSJ0DtuMX5HM1Q63YB+Fq0jAqIqLO4wxl6nWEow7eos0QQj3lfnpFhwHpcW3qKAuhwvH5PyCaamA+75Y2JSAinS4tB2ptGZrf+SO8ZTshVB+TyZ2kzz0bUL0tJS6OkiQZSurgNmUwiIjCjdpwBL7K/SfdnnP0DqDdxXWdOq9728dQm2shx9hhGDm7R0pQHL9An3vjiqC3R0REka3q6Axlv6MloiQDF+UjovDChDL1OlJ0AqD6IBz1p913SJYNxZVNqG/6scSDe9P78BVthjHvCn99xd5EnzUWlnl/APRGOFb8Cfv/dBnU+kqtwworuvi+kJMHwFP4BYQQUOsr0Pz+4/BVH9I6NCKibnNvXAHHyqdPun1A+tE6yp0oe+E9tAWu1YtbfRHXU5TUwVAGToR7y4dQaw/3ePtERBQZhBBtEsri6CK2ktGiVVhERF3ChDL1OnJ0PABANFaddt/czJZ9Cw62fOj1Hd4N97p3oPTPg37oucELMsTp4tMRdeH/gy4tF/qEdEgxXVtYqTcz5JwNtbYMvsO74Nn1LXwlBZAMfCNJRBHAYPF/QG6PUa9D/7RYFB7sWEJZ+DxwfrsYUlwKDMNnBCrKTjFOuAzQGeBa/64m7RMRUfhrcHjg9amwtpNQBj8HEFGYYUKZeh0pqiX5qXYgoZyVEgOzUfGXvZDtWTCMuRCmKdf3+tXeJVM0LHN+jz4/exqSxJeSzlL6nwHDmHmQY5Pg2b0auvRcyNFMzBNR+JMMZsDngfB5TrrP4AwbDpY3oNnpPe353Js/hKgvh2nS1ZqVWJItVphn3QbTWddq0j4REYW/2gYXALSuocySF0QUppgFol6nMzOUZVlCToYVhQeqoNYdhqTTwzh2HiS9Kdhhhg0mk7tGUowwjrsIan0FREMl9AMnaR0SEVFAHLvb4tiH5PbkZNogBLCruPaU51IbjsC98T0o2eOg9BkWyDA7TUkdDMlggeqoh/C4NI2FiIjCT82xhHLsjwllJS0X5rn3QI5N0iosIqIuYSaIeh+DBeY5v4fSwQRebqYNkzzfovHt+6E2Vp/+AKIOEqoKx4o/AQCU7HEaR0NEFBj+OpCnKHsxID0Wik4+bdkLX2kBIOtgPPOKQIbYZaqjHk1v3MUF+oiIqNNq2pmhLJmioaQMgqQYT3YYEVFIUrQOgKinSZIEJS23w/sPV/bDYipARcKZiD06u5koECRZhpI1BnJcCiQ930QSUWSQomyQk/oD4uT76BUdBqTHnnZhPv3gs6BkjYFkjApwlF0jm2OhZI6Ge8uH0A+aDNmaonVIREQUJmoaXJAlIC7a4H/OW7QJvor9MI67SMPIiIg6jzOUqVfy7PkOrg3LT7ufr7YUlo2vokhNwqcirwcio97GPOPWlsWeiIgihJKWi6gL/3DaZGtOhg2HyhvR5Gxba1l43fDs/BpCVUMmmXzMsQX6nKtfgRCnyJoTEREdp6bRBWuMCTr5xzSM99A2uLet0jAqIqKuYUKZeiVfaSE82z4+5T7C44Lz40WQdHpsSLkYO4rq+cGRiIiog043Zg7OsEIA2FVU22abe8uHcH75L/gq9gQnuG6QLXEwjr8YvuJt8O5fp3U4REQUJmoaXEiIa70Wj3A3+9ceICIKJ0woU68kRcdDOBsgvO6T7uOr3A+14QhM59yMrH4ZqG/2oKSyqQejJCIiCj9qYxUa/nkTPDu/OuV+/dLioFdkFJxQ9kJtqGxZiK/feCgpg4IZapfph5wDOb4vXN+9DqH6tA6HiIjCQG2DC4lWc+sn3Q4mlIkoLLGGMvVKcnQCAEA0VkM6yS25SloOoq58ErIpBkNinACAHQdr0CcpusfiJCIiCjeS3gSoXsDtOOV+ekXGgPQ4FB6sbfW8a/WrgCTDmBcaC/G1R5J1MJ19IyC1/JuIiOh0ahpcGD24vRnK5pMcQUQUujhDmXol6WhCWW2sarPNV7EPzu9eh1C9kE0xAICEOBOSbGbsOFDdo3ESERGFHYMZgAThbj7trjmZNhRXNqLR0VJH2Vu0Cd6DG2EYMw9yiC+Eq7NnQZeYBaGqEM5GrcMhIqIQ5nL70OzyIr6dkhfgDGUiCkOcoUy90rEPqeKEhLLqbIDjk0UAAOPofMD445/IkEwb1uwoh9enQtHxuxgiIqL2SJIM6E0Qp5mhDAA5GVYAwM6iGowdnATP7u8gW1NhGD4jyFEGhhACjpV/AYSAefbvIEmS1iEREQWc16fi5Y92oqhCmy/P9IoMj1fVpO1A8fla4j+x5IVh2IyjX8QSEYUXJpSpV5Ki4mHMuwJyUn//c0JV4fzsBYjmOljm3dtmVfkhWfH4YlMpDpQ1YECfuJ4OmYioQ1asWIG//e1v8Hq9uO6663DVVVe1u9+dd96JvLw8XHzxxT0cIfUGksHcoRnK2amxMOhlFB6sxdjBSTCd81OI5jpIuvB4iypJEpSMUXCtfgXe/eug7zde65CIiALujc/24OstZRiaZYNOg4k1BoMCt9vb4+0GWkq8BSMGJEI9ri/6wWdpGBERUdeFx7t1ogCTdAoMI2a2es69cTl8xdtgPOt66OzZbY7JybRBArDjYDUTykQUksrLy/HUU0/hnXfegcFgwIIFCzBhwgQMGDCg1T73338/1qxZg7y8PA2jpUgmGS2A23na/RSdjIF9rCg9WARfpR46ezakKFsPRBg4+iHT4Nn5FVxrXoPSd3hLDWkiogjxzZYyfLq+GDPG98WCcwdqEoPdHoPKygZN2g60hDizvy9CCHgPboQuvg/k2CSNIyMi6hzet0+9lu/wbngPbAQAeIu3w71+GZSBk6DPObvd/aPNevRNjkbBgZp2txMRaW316tXIy8uD1WqFxWLBzJkzsXLlylb7rFixAueeey7OP/98jaKk3sBy0f0wTf9lh/bNybBioutLNK34c4fKZIQaSdbBNOkaiKZquDcs1zocIqKA2V9Wj/99tBO5mTZcOq3/6Q+gzvE44Vz1DLz712sdCRFRp3GGMvVa7m2r4KsqQnTWaOgSM6Efdh6MZ8w/Zf3DIZnx+GT9Ibg8Phj1XNWdiEJLRUUF7Ha7/3FSUhK2bNnSap+bbroJALB+PT+8UPBIOn2H9x1hLIXVcAgVfWcjNkzrSOpSBkIZdBbcWz6CPmcK5LgUrUNql/fQ1jaJC/3wGdDZ0loWRDywqc0xuowR0GeNgVpXDvfmD9tsl+OSoB8+C5LMeSpEkaSuyY3n3tmKuCgDbp43FDr+jQec/0vUMB37iKh3Y0KZei0pOgFi/zqozXWQLXEwTWy/zujxcrNsWPlDEXYX12JYdkIPRElE1HGqqrb6UkwIEZRFwhISogN2Lrs9JmDn0lKk9AMITF9qv1sGV+luJF/8u1Pup3rdcOxZhlI1DhsNo5EXwJ9jT18T3+yfoGnXUMT06wdJDuyXzoHoi7euEoc+eQ6QZMh6o//5uPHnwGyPQd3+OtQe2tTmuJjUdFjtMXC6y1B+wnah+mDIHgF7UmyHXmv4dxJ6IqUfFFhen4q/vbsNTQ4P7r56LGIsBq1DikjHEsqSwaJxJEREnceEMvVacnQCIASaFv8G0dc+02YRvvYM6mOFTpZQcKCGCWUiCjkpKSlYt26d/3FlZSWSkgJfk6+qqhGqKrp9nkipiRgp/QAC1xdnWQk8+7ac9lyuDcvgrS3HuphLsGVXVcB+jtpcExnokwdXVTOExwXpuKRtdwSqL77KMkgxSTDP/DXkmET/840AGisbgOyzYcluW/bLA7S0b0iF5aqn22wXqoojRxrhLS2EHGU96exs/p2EnkjohyxLAf2Sk1q88eke7DpUi5/OHYLMFH7pECzHFq+VjEwoE1H44X0r1GtJUfEAAKX/+A4lkwHAaNChf1osdhxkHWUiCj0TJ07EmjVrUF1dDYfDgVWrVmHKlClah0W9kGQwA24HhDj5Fw/C64Znx+dQ+p2B2AEjUFbVjLpGVw9GGRzuLSvR9OY9EJ7TL0rYk3T2bFguebBVMjkQJFmGUH1wfvVvNL37ELwlOwJ6fiLqWV9vKcWnG4ox84y+yBsSmuV7IsaxhDJLXhBRGGJCmXotJWMkTNN/CdPUhZ06LjcrHkWHG9Do8AQpMiKirklOTsbtt9+Oa6+9FhdeeCHmzJmDESNGYOHChdi6davW4VFvYrAAQgW87pPuIikGWOY/BOPEq5CTYQMAFBbV9lCAwaNL6g/RWBUyC/QJnwfOr1+CWl8RlBI4QMvChJbZv4NsiYPjgyfh3vFZUNohouDaV1qPl48uwjd/KhfhCzbJGAVdxkhI5jitQyEi6jSWvKBeS9Ip0GeP6/RxuZk2LPtmP3YW1WDs4MDfSk5E1B35+fnIz89v9dyLL77YZr/HHnusp0KiXujY7bvC3dxu6QdfVRHk6ATIppZbqTNMKsxGHQqLajBhSHKPxhpoxy/QpwyaDJ0tTdN43FtWwlPwOZSsMZBjg/e+RY5NgmXeH+D49G9wffM/qNUlME68MuD1pIkoOOoaXVi0dCus0Ub8/MJhXISvB+iSB8Ay63atwyAi6hKOEkSd1C8tFka9jmUviIiITuLY7bvH6kMeT3jdcKx6Fo5Vz/qf08kyBvWxojBCxlbjhEsBvRGu1a+csuxHsKkNR+DesAJK1lgofYcHvT3JYIZ55m3Qj5gFT+EXUKsPBb1NIuo+r0/F80cX4fvlxcMRbdZrHVKvIDzOlv80HCeIiLqKCWWiTlJ0Mgb1taLgQGR86CUiIgo0XVouzHPughzdtl6ve/MHEA2VMIyZ2+r5nEwbymscqGkI/zrKsjkWxvEXw1eyA979azWLw7XmNQCAceKVPdamJMsw5S1A1KWPQJeYBSFUqI1VPdY+EXXea5/uxu7iOvxkdi4ykrkIX09xb1iOxpd+GbRyREREwcSEMlEX5GbacLi6OSI+9BIREQWabI6FkpbTptyFWl8B96b3oPSfACV9SKttP9ZRjowvbPW558AwZi50qTmatO89tAXeA+thGDMXcnRCj7cvx7Us5uXe+B6a3voDmvdv7vEYiOj0vt5cis83lGDWGRlhX3Io3Ai3gwvyEVHYYkKZqAuGZLV86N1xoFrjSIiIiEKPcDXBtW4pfEcOtHreuXoxICsw5i1oc0zfpGhYjErElL2QZBnGcRdDNsdCqL4eb19tOAI5vi8MI2b2eNvH0w84E3JUPA6/9jDc2z/VNBYiam1vaR1eXrUTQ7JsuGRqP63D6XWEuxk4uuYAEVG4YUKZqAv6JEUj2qxHQYR86CUiIgok4fPAvWEZfBX7/M/5jhyEr2gzjGPmQY6ytTlGliUMzrBGzAzlY3yHd6Pp9Tvhqynt0XYNQ86B5eI/QtJpWwtVjrXDMu9eWPqPhuvbl+H85n8QqlfTmIjo6CJ877QswnfzPC7Cp4WWGcpMKBNReOKoQdQFsiQhJ9OGgoM1XESBiIjoBD8uyufwP6dLzIRl3n3QD59+0uNyMmyorHWiqs4Z9Bh7ihSXDOF2wPXtyz3ynkFtOALXpvcgfB5Isi7o7XWEZDAj+dLfQz/ifHh2fAbXD29pHRJRr+b1qVj07jY0O71chE9Dwt3MkhdEFLaYUCbqoiGZNtQ0uHC4uu0K9kRERL2azgDIOsDdMkb6qoshhIAueQAkWTnpYYMzrAAip44ycHSBvjPmw1daAO++H4LenmvNa3BvWA7hqA96W50hyTqY8i6H6ZybYRg5GwAghKpxVES902uf7MYeLsKnOUmSIZlitQ6DiKhLmFAm6qLco3WUWfaCiIioNUmSIBksEG4H1PoKNC/9I9wbl5/2uGMlpSIpoQwA+pypkBMz4VrzWqtZ24HmLdJ2Ib6O0A/Ig2yOhepsQPPSB+At3qZ1SES9ylebS/H5xhLMmsBF+LRmmXsPzOferHUYRERdwoQyURclWc1IiDWi4EBkfeglIiIKCIMZwt3sX4hPP3jKaQ+RJQmD+1pReLA2+PH1IEmWYZp0DURzLVwbTp9Y7wrhdcO5+hXIcSkwDJ8VlDYCyusGVB8cH/4F7u2faB0NUYetWLECs2fPxowZM7B48eI22/ft24drrrkGc+fOxY033oi6ujoNomzf3pI6vLJqJ4Zm2TD/7P5ah0NERGGMCWWiLpIkCbmZ8SgsqoGqso4yERHR8QwjZgGyvmUhvrHtL8TXnpxMG6rqnThSG7yZvFrQJQ+AYdzFUDJGBuX87i0rIeorYJx0NSTdycuKhAo5OgGWufdC13cEXN++wsX6KCyUl5fjqaeewquvvop3330Xb7zxBvbs2ePfLoTAz3/+cyxcuBDLly9Hbm4u/vGPf2gY8Y/qGl1YtLRlEb6fzRsGWZa0DqlXE6oPTUvuhrvwS61DISLqEiaUibohN8uGJqcXRRUNWodCREQUUvSDJsNXVgDZlgb9sJMvxHeiY3WUCyKs7AUAGMfMhZKWAyFEQBfoE0JArdwPpd94KH2GBey8wSYZzDDPuBWGkbPh2fEZHB/8H5PKFNJWr16NvLw8WK1WWCwWzJw5EytXrvRv3759OywWC6ZMabkj4+abb8ZVV12lVbh+/kX4XF786pIRXIQvFLgdUGvLAI9L60iIiLok9KcvEIWw3MyjdZQP1CArhQsqEBERHePZ9Q1EwxGY5vz+lAvxnSg9MQoxFj0KD9birBFpQYxQG8LZCMenz0M/eAr0A/ICck5JkmCacSvgcwfkfD1JkmUYJ1wG2ZYGta68U78rPU1tqoHr25dR7KyD1+vzP2+Zew8kxQDnt6/AV76nzXHGiVdBSRkIz65v4N7WtryHftAkGIZNh6+6GM4v/tlmu2xLh3naQgBA07KHAV/bpHtXYnApOn8/ejKGcFZRUQG73e5/nJSUhC1btvgfFxUVITExEffccw8KCgrQr18//OEPf9Ai1FZePboI383zhqJvUrTW4RDgr6cvGcwaR0JE1DWh+46NKAxYo41IS4zCjoM1OD8vU+twiIiIQoYcnQDjhMuhpOV26jhJkjA4w4bCohoIISBJEXZbtsEC4WqC67vXoWSM7HYywVtaCAgVSvoQQDEGKMiepx802f9vz+7VkMyxITXbWggBx0dPQ609DHPmUPg8vjb7SMYoSJa4ts/LupZ/KMb2tx+9bpKstL/dGOX/t2yOO+Us7s7EoBgU+Nzeno8hjKmq2uo16cTXKK/Xix9++AGvvPIKhg8fjqeffhqPPfYYHnvssQ63kZAQ2ITvhr3V+GJjCS6ZNgAXTBkQ0HP3JLs9RusQAsZuj4HLV4kmAHH2BESFad8i5ZpESj+AyOlLpPQDiKy+nIgJZaJuys204evNpfB4VegVVpEhIiIC0FIruIv1gnMzrFhXWIHKWgeSbJYAR6YtSZZhmnwtmt99CK4Ny2DKW9DlcwmvG86v/g1JkqG79FFIcvi/DxGqCve2j6EeOQDjmVdCP/Q8zb9UEKp69LpdB+j0SMkZgsrKtuXOjOMuOuV59P3GQ99v/Em3y9YUWGbdfspzmGf86pTbOxOD3R7Tph89EUM4S0lJwbp16/yPKysrkZSU5H9st9uRmZmJ4cOHAwDmzJmDW2+9tVNtVFU1Bmx9lqpmD/7+zmYMzY7H+eP7tvt7Gw7a+10NV8f64i0/AgCod0poDsO+Rco1iZR+AJHTl0jpBxD+fZFl6ZRfcob/u04ijQ3JtMHtVbGvNHRWcCYiIgpngzNaSkoVFtVqG0iQ6JL6Q58zBZ6tH8NXXdLl87RaiC8CkslAS8LdMuf3UDJGwbV6MVzfvKRZXWUhVLjWLYXjo6chVBW6pH7QJfTVJBYKDRMnTsSaNWtQXV0Nh8OBVatW+eslA8Do0aNRXV2NwsJCAMBnn32GoUOHahJrbaMLf/rvD7DFGPGzuUO5CF+IEe5mACx5QUThKzLeeRJpaHCGFZIE7DgQeYsHERERaSE1wYK4KAMKD0bu2Go441LAYILr25e7tECf2lAJ98YVYbcQX0dIehNMM34Fw6gL4Cn4omWxPmdjj8YgvC44P/0b3BuWQTLHAELt0fYpNCUnJ+P222/HtddeiwsvvBBz5szBiBEjsHDhQmzduhUmkwmLFi3CfffdhwsuuADff/897rrrLk1i/du729Dk9OJXF3MRvlCkpA+FZf7DkK2Rt1YAEfUOLHlB1E0Wkx5ZKbEoOFiDyL3Bj4iIqOe01FG2oiBS6ygDkE0xMOUtgOqoB4QAOtlH1+pXAUmGMe+KIEWoLUmSYTzjUsjWNLi+ex2qox46U88sJqY21cDx0V+hHjkI44TLoR8xKyJ/B6lr8vPzkZ+f3+q5F1980f/vkSNH4q233urpsFpRVQFFJ+O3V45FHy7CF5IkvQm6+D5ah0FE1GVMKBMFwJAsG1Z+XwSHywuzkX9WRERE3ZWTacMPBRUor3EgJT6y6igfox98VpeOUx318FXuh2HMXMjR8QGOKrToB02Ckj0Wkt4E4XXDV7kfSurgoLWn1legefmjEB4nzDN/DSVzVNDaIgoWWZZwxxWjw75+ZyTzHFgPX8kOGCdezS+siCgsseQFUQDkZtrgUwV2HarVOhQiIqKIkHusjnIEl70AACEEXOvegeuHjs9olM2xiLrsTzAMnxnEyEKHpDcBANyb3oPjvcfg3rqqS2VCOtRWdDx0fYbCMu9eJpOJKGh8Zbvg2fkNk8lEFLaYUCYKgAHpcVB0Mgoi/EMvERFRT0mymWGNNqCwKLLHVkmSIJrr4N78AXzVxafd33NgA9TmWkgGMyRd77oryjByNpTM0XCteRWur/8L4QvMYn1CqHCtXwbfkQOQZAXmqQuhi+fie0QURO5mSMbIvPuGiHoHJpSJAsCg12FgnzguzEdERBQgkiQhJ9OGwqLaoM1GDRWGM+YDBvNpF+hT6yvh/PRvnZrNHEkkvQmm6b+EYdQceAq/hOODJ7q9WJ/wuOD85Hm41y+Fd+8PAYqUiOjUhNsByWDWOgwioi4LakJ5xYoVmD17NmbMmIHFixe32f7JJ59g3rx5mDt3Lm655RbU1dUBAJYuXYrJkydj3rx5mDdvHp566qlghkkUELmZNhRXNqK+ya11KERERBEhJ8OG+iY3yqqatQ4lqGRTDIzj58NXthPevd+ddD/XmqML8Y27uAejCy0ti/XNh2naT+Gr2AvX+ne7fC61sRrNKx6Fd/96GPMuh+GMSwMXKBHRKQi3AzBwhjIRha+g3SdXXl6Op556Cu+88w4MBgMWLFiACRMmYMCAAQCAxsZG/PGPf8Tbb7+N5ORk/PWvf8Wzzz6L++67D9u2bcNdd92FOXPmBCs8ooDLzbIBXwFLv96HBecMhNGg0zokIiKisJaTebSOclEN0hKjNI4muPQ5Z8Oz8yu4vnsDSsaoNjPXvAc3wXtwI4wTLov4hfg6Qj9wImRrGmRrKoDOz/bzVeyD46O/QnhdMM/6NZSMUUGKlIioLeFuhmSK0ToMIqIuC9oM5dWrVyMvLw9WqxUWiwUzZ87EypUr/ds9Hg/uv/9+JCcnAwAGDx6MsrIyAMDWrVuxdOlS5Ofn43e/+51/5jJRKMtOjcW00en4clMp7vvnd9i0+4jWIREREYU1e5wJ8bHGiF+YDwAkWYZp0jXQJfWD8La+20l43XCuXgzZmgb9sBkaRRh6dPYsSHoj1PoKNL1+J9xbP+pweRS14QigGGCZ9wcmk4moxxlGXQDD0HO1DoOIqMuCllCuqKiA3W73P05KSkJ5ebn/sc1mw/Tp0wEATqcT//jHP3DeeecBAOx2O2655RYsX74cqampePDBB4MVJlHAyJKEa2YOxl1XjYHRoOCZt7fguXe2orreqXVoREREYUmSJORktNRRViO8jjIA6JL6wTzjVsiWuFbP+8oKIRqrYJx0da9biK8jJHMcdCmD4FrzGlxf/+eki/UJocJ7cBMAQN//DERd+gh08ek9GCkRUQt99jgoGSO1DoOIqMuC9o5UVVVIkuR/LIRo9fiYhoYG/OIXv0BOTg4uuugiAMCiRYv822+66SZ/4rmjEhKiuxh1W3Z75NyGEil9CfV+2O0xOGNEOt79cg9e/3gX/vCv73HVrFzMmZQNnU5us28kiJR+AJHTl0jpBxBZfSGizsvJsGH1tsMoPdKEPvbAvccLZd7ibfDsWQPT2TcBAJS+IxC14HHIMYkaRxaaJL0Rpum/gHvdUrg3roBaVw7T9F9CPu52cuFxwfn5P+A9sB7mC+6Ekj4EkmLQMGoi6q2EEPDuXg05MYtfahFR2ApaQjklJQXr1q3zP66srERSUlKrfSoqKnDjjTciLy8P99xzD4CWBPPbb7+N66+/HkDLi61O17latFVVjVDV7s9isdtjUFnZ0O3zhIJI6Us49WPqiFQMzbDilVW78M9l27DquwO4blYOslNjAYRXX04lUvoBRE5fIqUfQPj3RZalgH7JSdQb5WRYAQCFB2t6TUJZbTgC765v4U0fiqa6BIjYgUwmn4YkyTCOvwSyLQ3OL/+F5mWPIGr+Q5B0eqiNVXB89AzU6iIY866ALi1X63CJqDfzeeD84kUYxs9nQpmIwlbQSl5MnDgRa9asQXV1NRwOB1atWoUpU6b4t/t8Ptx88804//zzce+99/pnL1ssFvzzn//E5s2bAQCvvPJKp2coE4UKu9WM2y4dgVsuHIa6JjcefmkdXlm1E83O9m/FJCIiotYSrWYkxplQWFSrdSg9Rj94CmR7Npzf/A/lS/4Ez66vtQ4pbOgHnAlL/t0wjroAkk4PX8VeNC99AGp9Ocwzb4NhxMx275okIuopwt0MAJ1aSJSIKNQEbYZycnIybr/9dlx77bXweDyYP38+RowYgYULF+LWW2/F4cOHsWPHDvh8Pnz00UcAgGHDhuGRRx7B008/jT/+8Y9wOp3IysrC448/HqwwiYJOkiSMy0nC0Ox4vPPVPny2vhjrd1XiZxeNwOC0GH6oISIiOo2cDBs27q6EKgTkXjBuHlugr/ndh6BP7AP9wElahxRWdEn9oUvqDwBw7/gcUIywzPk9dDbOBCSiEOB2AAAko0XjQIiIui6oq3rk5+cjPz+/1XMvvvgiAGD48OEoLCxs97hx48Zh6dKlwQyNqMeZjQqumj4IE4el4H8rd+Lxl9dhWL94XD1jMJKs/HaaiIjoZHIyrfhmaxmKKxqRkdw76qrrkvrBPOs2JPYbgFovF+LrCiFU6JL6w5h3eat6ykREWuIMZSKKBEEreUFE7ctOjcUfrhuHhRcOw57iOvzhn9/jvdUH4PWpWodGREQUknIybADQq8peAICSMRJ6W4rWYYQtSZJhGDKNyWQiCini6AxlGKK0DYSIqBuYUCbSgCxLmHtWfzyyMA8j+ifgna/24Y//WYtdh2q1Do2IiCjkxMeakGQ1o/BgjdahEBERdYtkioYyIA9ylFXrUIiIuowJZSIN2WKM+MVFw/Hr+SPgcvvw2OIN+Pf7BWhodmsdGhERUUjJybRi56EaHDzcoHUoREREXaZLzIL5nJshx9i1DoWIqMuYUCYKASMHJOLhmybg/LwMrNl+GPe++D2+2VIGIYTWoREREYWEaaP7QK/o8OBLa/H6p7vhdHu1DomIiKjThLMRalMNP+sRUVhjQpkoRBgNOlw6dQDuv348UuIt+PcHBfjzqxtRXtOsdWgURD5Vhco3k0REp5WZEoNHFk7A2SPTsGrtIdz3z++xcXel1mERERF1invrR2h69Tdah0FE1C1MKBOFmD5J0bjr6jG4/vwclFQ24pH/rcfe0jqtwwpLQgiUHmkK2YTtrkO1+P3f1+D/Xt8Ej9endThERCEvyqTHtbNycM/VY2E2Knj27a147p2tqK53ah0aERFRhwh3M6A3Q5IkrUMhIuoyJpSJQpAsSZgyMg33XTcOZqMOT7y2EVv2HtE6rLAihMAbn+3Bff/8Hk++thFH6hxah+SnqgIrvt2PP7+6AUIABQdr8I8VO6CqoZn4JiIKNQP6xOH+68dj/tT+2LavCvf+83t8vO4QX0eJiCjkCbcDktGidRhERN3ChDJRCEu2WXDPNeOQGh+FZ97aim+2lGkdUljwqSr+/UEBVq09hNEDE7H/cAPu//cPWLPtsOa1yuoaXfi/NzZh6df7cUZuMh6+aQIWnDsQ63dWYvEnuzSPj4goXCg6GbPzMvHgTRMwsE8cXvtkNx763zocOFyvdWhEREQnJVzNkAxmrcMgIuoWResAiOjU4qIMuPPK0Vi0dCv+/UEB6ppcmJ2XyVukTsLj9eHvy7Zj4+4jmDc5G3MnZaGyzol/vrcDL763A5v2HME1Mwcj2qzv8di276/Giyu2w+n24frzc3DWiFRIkoQZ4/uirtGFD78vgjXaiPyJWT0eGxFRuEqymnH7pSOxtrCiJan80jrkn1WNmWP7wGzkW10iIgoxHgckA2coE1F447tsojBgNiq47dKR+Nf7BXj7y32oa3RjwXkDITOp3IrD5cWzb29BYVEtrjxvIM4b1xdAS7LhrivH4MPvD+Ldr/djd3EtbpwzBEOz4nskLp+q4t2v9+ODNQeRmhiFO64YinR7dKt9LpnaH7WNbiz9ah/iogyYMjKtR2IjIooEkiThjNxkDMuOx9tf7sOKr/fh640luGr6IIwZZNc6PCIiIj/JYAFY8oKIwhwTykRhQtHJWJg/BLEWAz5edwh1TW7cNGcI9Aor1wBAfbMbTy3ZjEPljViYPwRnDk1ptV2WJVxwZhaGZSfgHyu24/9e34TzxvXB/LP7w6DXBS2u6non/r58O/YU12HKyFRccd4gGNtpT5Yk/GR2Dhqa3XhpZSFiLQaMGpgYtLiIiCKRxaTHNTMHY/ZZ/fDX1zbiuXe2YvTARFw1fRDiY01ah0dERATzzF9rHQIRUbcxE0UURmRJwoJzB+DSaf2xtrACT7+5GQ6XV+uwNFdd78Rjr2xA6ZEm/OqS4W2SycfLTInB/dePx7lj++CTdcV48KV1OHi4IShxbdxdifv//QMOVTTip3OH4Przc9tNJh+j6GTcctEwZKXE4O/LtmFPSV1Q4iIiinQ5mfH4f9ePw6XT+mP7/mrc++L3WLX2EHyqqnVoRERERERhjwllojAjSRLOn5CJm+bkYtehWvx58QbUNbq0DkszZVVNePSV9ahrcuG3l4/CyAGnn9Vr0Otw1fRB+M3lI9Hk9ODh/63D+2sOwKcGZkE8j1fFq5/swrNvb0VinBl//Ml45A05eZL7eCaDgl9fOhK2GCP++uZmlB5pCkhMRES9jaKTcf6ETDx80wQMzrDi9U934+GX1mN/GRftIyIibQihonHx7XBvXaV1KERE3cKEMlGYmjgsFbfOH4HDNc145OX1KK9u1jqkHnfgcD3+9MoGeL0qfn/lGAzqa+3U8cOyE/DQjRMwepAdb3+5D/c8/w0qax3diqmiphmPvrIen6wrxnlj++Cea8Yi2da5GmmxFgN+c/koKDoZf1myCTUNvfcLAyKi7kq0mvHr+SNwy4XDUNvkwsP/W4dXP97FO3yIiKjHCbcToqkGED6tQyEi6hYmlInC2PB+CbjzijFwun145OXeNeuq8GANHn91I4x6He6+eiwykmO6dJ5osx4/nzcUN83JxYGyetz/7x/wzZYyCNH52crf7yjHH/+zFkdqHfjlxcNx5fRBXa5xbbeacdulI9Hs9OIvSzahyenp0nmIiKjl7p5xOUl45KY8TBudjk/XF+O+f36P9TsrtQ6NiIh6EdV1dBKQgYvyEVF4Y0KZKMz1S4vFPdeMhcmgw+OvbsS2fVVahxR0G3dV4i9LNiM+1tQyAzi+e2/IJEnCxGGpePa305CRHIN/f1CA55duQ0Ozu0PHuzw+/PfDQrywfDv62KPxx5+cgTGD7N2KCWip9/yri4fjcFUznn1rC9wezmQgIuoOi0nB1TMG495rxyHarMeipVvxwXcHtQ6LiIh6CdXZklCWDGaNIyEi6h4mlIkiQEq8BfdcMxZJNjP++tYWrNl2WOuQgubbrWVYtHQbMpKjcddVY2CLMQbs3EnxFtx5xWhcOq0/Nu05gv/3rx+w9TQJ+pIjTXj4pXX4anMpZudl4s4rRyMhzhSwmHKz4rEwfwh2F9fhHyt2QA1QnWciot6sX1os/t/14zBhSDLe+mIvPll3SOuQiIioF1BdLeujSJyhTERhjglloghhjTbi91eOwcA+cXjxvR1Y+X2R1iEF3KofivCv9wuQk2nF7xaMQrRZH/A2ZLll0cM/XNcye+2pJZvx8qqdcJ0wO1gIga83l+Kh/65FfbMbv7lsJOZP7Q9FF/iX1TNyk7HgvIHYsKsSr3y8q0vlOIiIqDWdLOPGC3IxZpAdr36yG19tLtU6JCIiinA/zlBmQpmIwpuidQBEFDgWk4LbLxuFF9/bgSWf70FtowuXnTMAsiRpHVq3CCGw9Ot9eG/1QYwbbMfC/KFdrk3cURnJMfh/14/D21/uw6q1h1BwoAYL84cgOzUWDpcXL6/aie+2lyM304aF+UNgjQ7cTOn2TB/XF7WNLnz4XRGs0QbMnZQd1PaIiHoDRSfjZ3OH4rl3tuKlDwuhV2ScOTRF67CIiChCmfuNQNSVf4Fk7tr6L0REoYIJZaIIo1dk3Dx3KF6zGLBq7SHUN7lxwwW5QZk52xNUVeCVj3fhi40lmDIyDdfOHAxZ7pkEuV7RYcG5AzGifwL+9X4BHn15PaaP74uNuypRUevAhWdlY86ZWT0Wz/yz+6O+0Y13v96PuCgDzh6V3iPtEhFFMr0i4xcXDcPTb27Gv94rgF4nY1xOktZhERFRBJJ0esjR8VqHQUTUbeGZYSKiU5JlCVdOH4iLp/TDdzvK8dc3N8Ph8modVqd5fSr+sWI7vthYgtl5mbhuVs8lk483JCseD954BsYOtmPl90VweXy484rRmDspu0fjkSQJ152fg+H9EvC/j3Zi467KHmubiCiSGfQ63Dp/BPqlxeKF5duxec8RrUMiIqII1FiwBo7PXoBQudg2EYU3JpSJIpQkSZgzMQs/mZ2DgoO1ePy1jahrcmsdVoe53D4889YW/FBQgUun9cf8qf0haVi6I8qkx83zhuGuq8bgwRsnYHCGTZM4FJ2MWy4chqyUWPx9+XbsLq7VJA4iokhjMii47dKR6JMUjUVLt2HHgWqtQyIiogjjKtsD7761kGSd1qEQEXULE8pEEe6sEWn45SXDUXakCX96eT2KyhtCflG3JqcHT76xEdsPVOP683Nw/oRMrUPyG9TXGpTFADvDaNDhtktHID7WhGfe2oKSykZN4yEiihQWk4LfXj4KKfFmPPP2Fuw6VKt1SEREFEFUZzMkIxfkI6Lwx4QyUS8wakAifnfFaDQ5Pfjjf9bitme/wTNvbcF7qw+g4GANnO7QKYdR2+jCY4s34ODhBtxy4TBMGZmmdUghKcZiwG8uGwlFJ+MvSzajut6pdUhERBEh2qzHbxeMRnyMCU+/uRn7y+q1DomIiCKE6moCDGatwyAi6jYuykfUSwxIj8ODN07A5j1HsLe0DntL6rHpaI1ISQL62KPRPy0W/dPj0C8tFinxlh4pMeF0e1FR40BlrQMVNQ58vrEEDc0e3HbpSAzJ4oIVp2K3mnH7ZSPx51c34C9LNuPuq8cgyqTt7GkiokgQF2XAHVeMxmOL1+Mvb2zCHVeMRkZyjNZhERFRmFOdzZAMnKFMROGPCWWiXsQWY8TU0emYOjodANDo8GBfaT32ldZhb0kdvi8oxxebSgEAUSbFn1zunx6HfqmxMBu79pLR6PCgosaBitrmlv/XOFBxNIFcf0Jd58Q4E+64YjT6pcV2r7O9REZyDH558Qg8tWQTnnlrC357+SitQyIiigi2GCPuWDAaf1q8AU++vgm/v2oM0hOjtA6LiIjCmOpqYkKZiCICE8pEvVi0WY8R/RMwon8CAEAVAmVHmrC3tB57S+qwr7QeW/dWQQCQAKQlRqF/eiz6pcWhf3ocUhNa3gwJIVDb6EZlrQPlNc3+2cbH/mt2tS6pYYsxIslqxsj+CUiymZFksyDJaobdaobFxJelzsrNtGFh/lD8/d1teGH5dty/8EytQyIiigiJVjPuvGI0Hlu8AU++vhF3XTUGyTYmAoiIqGviz74CtSxVR0QRgJkbIvKTJQnp9mik26P9tYubnV7sL6v3l8lYv7MSX20uAwCYjQoSrWaUVzfB7VFbnScxzgS7zYwJabFIsppbEsdHk8YGPVc1DrTxOUmoO28gXv1kN/7wwhqkxpsRF21AXJQBcVFGxEUZEBttQLRZD7kHSpkQEUWK5HgLfnfFaPx58QY8+dpG/P6qMUiMY/1Ll8eHHwrKUVPvQt6wFCRZ+TMhIjodc/YINFY2aB0GEVG3MaFMRKdkMSkYmh2Podkt9YyFEDhc3Yx9R2cxOzwqcjOs/oRxks2M+FgTFB3X/Oxp543rC49XxVdbyrDzYDXcXrXNPjpZQmyUAbFRx5LNhqOJZ+Nx/255bDQw8U9EBADpiVH47eWj8MRrG/Hkay3lL2wxRq3D0kR5TTM+31CCb7eWocnZcgfSsm/2Y1i/BJwzJh3D+yVAlvnFJRFRe+rWfQifOQ26pH5ah0JE1C1MKBNRp0iShNSEKKQmRGHS8FTY7TGo5LfsIeP8vExcmz8MFRX1cLp9qG9yo7bRhbomN+qa3KhvcqOuseXftY0uHCxvQH2TG0K0PZfJoENclAF2mxnjBydh7OAkliQhol4rMyUGt18+Ek++vglPvr4Rv79yDGKjDFqH1SNUVWDL3ip8tqEY2/ZXQydLGDPIjnPGpCPJZsGXm0rw5eZS/PWtLUiMM2Ha6HRMHpGKGEvv+PkQEXWEUL2o+uifMIy9iAllIgp7zAwQEUUgSZJgNiowGxUkx5+63qeqCjQ4PKhrdLUknI/91+hGXZMLBw434D8fFuLlVbswamAizhyajOH9EjgLnYh6nf5pcbht/gg8tWQz/u+NTbjjitGINuu1Dito6pvd+HpzKb7YWIqqeies0QZcODkbZ41MazVD+8Kz+mHOxCxs3H0En60vxptf7MXSr/fjjNwkTBuTjn6pXGiXiAjultrJkpG1+Iko/DGhTETUy8my5C9/0R4hBPaXNWDN9sP4oaAc6worEG3WY3xuEs4cmoL+abGQWJeZiHqJwRk2/OqSEfjrW5vx1JJN+N2C0TAbI+cttRACe0vr8fmGYqwtrIDXJ5CTYcXl5wzAqIGJJ/0yUdHJGJ+ThPE5SSipbMRnG0uwetthrN52GJkpMZg3pT9y+8bByHUUiKiXEu5mAIBkYM15Igp/kfPul4iIgkKSJPRLi0W/tFhcfs4A7DhQjTXby/HtljJ8vqEEdqsJZw5NQd7QFKScZjY0EVEkGJodj1suHI5FS7fi6Tc34zeXjQr7uvMujw/f7yjHZxuKUVTeCJNBh7NHpmPqmHSkJ0Z16lzp9mhcM2Mw5p/dH2u2H8ZnG0rwzJJNiDIpmDQ8FdPGpCPZxvGCiHqXYwllGPj6R0ThjwllIiLqMEUnY0T/RIzonwiHy4sNuyqxZvthrPj2AJZ/ewDZqbE4c2gyzshN7jW1RYmodxo1MBE/nTsUf1+2Dc+8vQW3XToCeiX8ksrl1c34fGMJvtlShmaXF+n2KFwzczDyhiR3e+a12ajgnDF9MG10Osob3Hjns934dH0xVq09hGHZ8ThnTB+M6M9F/IiodxBuBwDOUCaiyMCEMhERdYnZ2DLTbNLwVNQ0uPD9jnJ8t/0wXv1kN17/dA+G9YtH3tBkjB5o5y3ORBSRxuckwePNxb/eK8Cipdvwy4uHh0V9eVUV2LznCD7bWILtRxfZGzvYjnPG9MHAPnEBL2MkSRKG909ESqwRNQ0ufLW5FF9uKsEzb29BQqwJU0en4ayRaYjlIn5EFMEkcyxix86CLyZR61CIiLqNCWUiIuo2W4wRsyZkYNaEDBRXNuK77eX4bsdh/GN5FYwGHcYOsuPMYSnIzbBxJhoRRZSJw1Lh9qr438qdeGH5dszOywzo+WscXtTWNgfkXKoQKDhQgy83laCq3gVbjBEXnpWNKSPTYI02nv4EAWCLMWLe5GxccGYmNu0+gs82FOPtL/dh2Tf7MT4nCROHpcJiCu2PKKaonvlZEVFk0dnSkThrISorG7QOhYio20L73RoREYWdPvZozJ8ajYvP7oddRbVYs/0w1u2swOpthxEXbcC4QUmIjdJDr+ig6CToFRl6RYaik/3/1utkKIqMBreKxgaH//Hx22QuBEhEIWLqqHS4PSpe/3Q31u+s1Dqc08rNtGHBuQMxamAidLI2M6oVnYxxOUkYl5OEkiNN+HxDMVZvO4w128s1iaezkqxmZKXGIDs1FtmpschMjgn7OtpEFFxqcy3clbUQIhaSFPp3sxARnQoTykREFBSyJCEn04acTBuunjEIm/dUYc32w/hycwm8PtHt8+tkCYoiw6jXYeLQFMyZmAmLSR+AyImIOm/G+L4Y2CcO9U3ugJ43Ls6MujpHwM6XHG8JuQVU0xOjcPWMwbjk7P7YU1IHVe3+GBEsqhCod/qwbXcl9pTU4YeCCgCAJLX0Iys1Fv2OJpnT7VFhUQKFiHqGp+ALFK9/F9E3/QvgvAgiCnNMKBMRUdDpFZ1/JpoQAj5VwOtT4fG2/Of/t0+F1yvg8frg8amwRBlxpLrp6D7i6P4+/7+9PhVV9U589EMRvtlahnmTs3H2qDR+gCciTWSnxgb8nHZ7TK+5PdpsVDC8X4LWYZyW3R6Ds4enAADqGl3YX9aA/WX12H+4Hpt2H8E3W8oAtMzCzkiOPjqLuWU2c3K8hXfYEPVSwu2AZDBBknk3AxGFPyaUiYioR0mSBEUnQdHJMJ1m/aWOJlKKyhvwxmd7sPjjXfh0fTEundYfowYkBnxhKSIiouPFRRsxaqARowa2LLIlhEBlnRMHyupbksxlDfhmSxk+XV8MADAbdchKiW0pl5ESi35psbDGBLYmswRw/CMKQcLVDNkYpXUYREQBwYQyERGFvYzkGPxuwShs3lOFJZ/vwbNvb0VOhhWXnzMQmSkxWodHRES9hCRJSLKakWQ144zcZACAqgqUVjX5E8z7y+qx6odD8AWptIfZqCArJabVzGhbjJFJZiKtuZshm0Kr5BARUVcxoUxERBFBkiSMGpiIYf3i8eWmUiz7Zj8e/O9aTByegoun9IctwDPAiIiIOkKWJfSxR6OPPRpnjWh5zuP14VBFS5K5yeEJaHu1TW7sL6vHRz8U+ZPWsVEG9EuNbbWQoD2grRLR6QiPAzojE8pEFBmYUCYiooii6GScO7YPzhyajPfWHMQn6w5hbWEFZp2RgVkTMmAycOgjIiJt6RUd+qW1lLwIFo/Xh6KKRhw4VuO5rB6b9xzBsXnRKQkWZCRFI+to6Y3M5BgYDaztShQsUpQNBotZ6zCIiAKCn6qJiCgiWUx6XDZtAKaNTsfbX+7F8m8P4MvNpbj4rH6YNDwVssxbf4mIKHLpFR36p8Whf1qc/zmHy4sDhxtwoKweJdXN2HmgGj8UVAAAJAlIS4xCdkosstNaymX0sUdzoVuiADFPXdirFlolosjGhDIREUU0u9WMm+cNw3nj6vDGp7vxnw8L8fG6Ylx+7gAMzYrXOjwiIqIeYzYqyM20ITfT5k9s1R0tkXHgaI3nTXuO4JutZQBa7vrpmxTdqlxGSoIFMusxExER9WpBTSivWLECf/vb3+D1enHdddfhqquuarW9oKAA9957L5qamjBu3Dg88MADUBQFpaWluOOOO1BVVYXs7Gw8+eSTiIriaqhERNR1A9LjcM81Y7G2sAJvfbEX//f6Jozon4DLpg1AWmLkjDFdHXuJiKh3iosyYNSARIwakAgAEELgSJ3zaJK5AfvK6vHN1jJ8uqEYAGA26pCZ/GMt5uzUWMTHctE/olMRQqDxpVugnHkhMHim1uEQEXVb0D5BlpeX46mnnsI777wDg8GABQsWYMKECRgwYIB/nzvuuAMPP/wwRo0ahXvuuQdLlizBlVdeiQceeABXXnklLrjgAixatAjPP/887rjjjmCFSkREvYQkSTgjNxmjBybik/XFeG/1Afy/f/2As0enYd7kbMRaDFqH2C3dGXuJiIiAlrHSbjXDbjXjjNxkAICqCpRVNWH/cfWYV6099OOifxa9P7mcldpSLiMmzMdUooDyuQG3A5LMEjJEFBmCllBevXo18vLyYLVaAQAzZ87EypUr8ctf/hIAUFJSAqfTiVGjRgEALr74YjzzzDO49NJLsXbtWixatMj//NVXX82EMhERBYxe0eH8CZmYNDwVy7/Zjy82luK77Ycx58wsnDeuD/RKeC5K1NWxlwllIiI6FVmWkG6PRro9GpNHpAIAPF4VxZWN2Fd6tFzG4QZs2VvlX/QvMc503CzmGGSmxHBhXOq1hNsBAJCNFo0jISIKjKCN6BUVFbDb7f7HSUlJ2LJly0m32+12lJeXo6amBtHR0f7bb489T0REFGixFgOunjEY547tgzc/34s3v9iLzzaU4JcXD0dmSozW4XVaV8deIiKiztIrsj9hfIzD5cXBww3Yf7jeP5t5beFxi/4lRCEn04bLpvUP2y9vw4WvYi8qf1gDp9Pd6nnZ3g+G3KkQria4vn+j7YEGC0x5CwAArrVvQzjq2u4y7mLIFis8u76F7/DONtuVgZOgpA6Gr2IvPIVfttne2RgqhaNNP3o6hu7+HNxbPmpp1xQ5ZdaIqHcLWkJZVdVWdbSEEK0en2z7ifsB6HQ9roSE6C5G3ZbdHn4JhZOJlL5ESj+AyOlLpPQDiJy+REo/gJ7pi90egxE5Kdi8uxJvfroLDp8Iy59hV8fezuI421ak9AOInL5ESj+AyOlLpPQDiJy+BKMfGX1sOOu4x3WNLuw+VIvdRTXYdagWRRWNiLVGIdqsD3jb9CO1qRauPRugqmqr5xW5JQUgfB54i7a0OU4yx/n/7SsthNpQ2WYfw6g5Lduri9s9hy41xx9De9s7G0NzU1WbfvR0DN39OfgO74IUmwxDcjYcos2uRERhJ2gJ5ZSUFKxbt87/uLKyEklJSa22V1b++KJ85MgRJCUlIT4+Hg0NDfD5fNDpdG2O64iqqkaoavdfpY+tfBwJIqUvkdIPIHL6Ein9ACKnL5HSD6Dn+5JmNeHXl4wAgIC0K8tSQJOvp9PVsbezOM62Fin9ACKnL5HSDyBy+hIp/QAipy892Y/MRAsyEy04b0w6AMDR6ISj0dnt8/b0OBtO9NljkXbG1JNeY9liRfTVT5/yHJZ5955yuynvciDv8lPGoM8ee9LtHY3hVL+rPRXDqXQmBkNiDBABrx9EREGrCD9x4kSsWbMG1dXVcDgcWLVqFaZMmeLfnp6eDqPRiPXr1wMAli1bhilTpkCv12PcuHH44IMPAADvvvtuq+OIiIiofV0de4mIiIiIiIg6KmgJ5eTkZNx+++249tprceGFF2LOnDkYMWIEFi5ciK1btwIAnnzySfzpT3/CrFmz0NzcjGuvvRYAcP/992PJkiWYPXs21q1bh9tuuy1YYRIREUWM7oy9RERERERERB0R1GV28/PzkZ+f3+q5F1980f/vnJwcvPXWW22OS09Px8svvxzM0IiIiCJSV8deIiIiIiIioo4I2gxlIiIiIiIiIiIiIoosTCgTERERERERERERUYcwoUxEREREREREREREHcKEMhERERERERERERF1CBPKRERERERERERERNQhTCgTERERERERERERUYcwoUxEREREREREREREHcKEMhERERERERERERF1CBPKRERERERERB2wYsUKzJ49GzNmzMDixYvbbH/uuecwbdo0zJs3D/PmzWt3HyIionCnaB0AERERERERUagrLy/HU089hXfeeQcGgwELFizAhAkTMGDAAP8+27Ztw1/+8heMHj1aw0iJiIiCizOUiYiIiIiIiE5j9erVyMvLg9VqhcViwcyZM7Fy5cpW+2zbtg0vvPAC8vPz8eCDD8LlcmkULRERUfAwoUxERERERER0GhUVFbDb7f7HSUlJKC8v9z9uampCbm4u7rjjDixduhT19fV4/vnntQiViIgoqFjygoiIiIiIiOg0VFWFJEn+x0KIVo+joqLw4osv+h/fcMMNuOeee3D77bd3uI2EhOjABHuU3R4T0PNpJVL6AUROX9iP0BMpfYmUfgCR1ZcTMaFMREREREREdBopKSlYt26d/3FlZSWSkpL8j0tLS7F69WrMnz8fQEvCWVE695G7qqoRqioCEq/dHoPKyoaAnEtLkdIPIHL6wn6EnkjpS6T0Awj/vsiydMovOSMyoSzL0ul30uBcWouUvkRKP4DI6Uuk9AOInL5ESj+A8O5LOMd+Khxn24qUfgCR05dI6QcQOX2JlH4AkdOXcO9HT8c/ceJEPPvss6iurobZbMaqVavw0EMP+bebTCY88cQTmDBhAvr06YPFixdj+vTpnWoj0H0K92t8TKT0A4icvrAfoSdS+hIp/QDCuy+ni10SQgTm608iIiIiIiKiCLZixQq88MIL8Hg8mD9/PhYuXIiFCxfi1ltvxfDhw/HRRx/h2WefhcfjwZgxY/DAAw/AYDBoHTYREVFAMaFMRERERERERERERB0iax0AEREREREREREREYUHJpSJiIiIiIiIiIiIqEOYUCYiIiIiIiIiIiKiDmFCmYiIiIiIiIiIiIg6hAllIiIiIiIiIiIiIuoQJpSJiIiIiIiIiIiIqEOYUCYiIiIiIiIiIiKiDmFCmYiIiIiIiIiIiIg6hAllACtWrMDs2bMxY8YMLF68uM32goICXHzxxZg5cybuvfdeeL1eDaI8veeeew4XXHABLrjgAjz++OPtbp82bRrmzZuHefPmtdvXUHHNNdfgggsu8Me6efPmVtvD5Zq8+eab/j7MmzcPY8eOxYMPPthqn1C/Lo2NjZgzZw6Ki4sBAKtXr0Z+fj5mzJiBp556qt1jSktLcdVVV2HWrFn4+c9/jqampp4MuV0n9uONN97AnDlzkJ+fj7vvvhtut7vNMUuXLsXkyZP91+Zk/e1pJ/bl7rvvxowZM/xxfvzxx22OCfVr8uWXX7b6W8nLy8PPfvazNseE6jWhU+M4G3oiYZzlGBs6r+dA5IyzkTLGAhxne4tIGWOByBlnI2GMBTjOhtJreqSMsUDkjLMcYwGIXu7w4cNi2rRpoqamRjQ1NYn8/Hyxe/fuVvtccMEFYuPGjUIIIe6++26xePFiDSI9tW+//VZcfvnlwuVyCbfbLa699lqxatWqVvv87Gc/Exs2bNAowo5TVVVMnjxZeDyek+4TDtfkRLt27RLTp08XVVVVrZ4P5euyadMmMWfOHDF06FBx6NAh4XA4xNlnny2KioqEx+MRN9xwg/jiiy/aHPfTn/5UvPfee0IIIZ577jnx+OOP93TorZzYj3379onp06eLhoYGoaqquPPOO8V//vOfNsc9+OCDYsWKFT0f8Cmc2BchhJgzZ44oLy8/5XGhfk2OV1FRIc4991yxf//+NseF4jWhU+M4G3oicZzlGKutSBlnI2WMFYLjbG8RKWOsEJEzzkbiGCsEx1ktRcoYK0TkjLMcY1v0+hnKq1evRl5eHqxWKywWC2bOnImVK1f6t5eUlMDpdGLUqFEAgIsvvrjV9lBht9tx1113wWAwQK/Xo3///igtLW21z7Zt2/DCCy8gPz8fDz74IFwul0bRntq+ffsAADfccAPmzp2LV155pdX2cLkmJ/rjH/+I22+/HfHx8a2eD+XrsmTJEtx///1ISkoCAGzZsgWZmZno27cvFEVBfn5+m5+9x+PB2rVrMXPmTAChcX1O7IfBYMD999+P6OhoSJKEQYMGtfl7AYCtW7di6dKlyM/Px+9+9zvU1dX1dOhtnNgXh8OB0tJS3HPPPcjPz8czzzwDVVVbHRMO1+R4jz/+OBYsWICsrKw220LxmtCpcZwNPZE4znKM1VakjLORMsYCHGd7i0gZY4HIGWcjcYwFOM5qKVLGWCByxlmOsS16fUK5oqICdrvd/zgpKQnl5eUn3W6321ttDxUDBw70D0oHDhzAhx9+iLPPPtu/vampCbm5ubjjjjuwdOlS1NfX4/nnn9co2lOrr6/HmWeeiUWLFuG///0vXn/9dXz77bf+7eFyTY63evVqOJ1OnH/++a2eD/Xr8sgjj2DcuHH+x6f7ewGAmpoaREdHQ1EUAKFxfU7sR3p6OiZNmgQAqK6uxuLFi3Huuee2Oc5ut+OWW27B8uXLkZqa2uYWLy2c2JcjR44gLy8Pjz76KJYsWYJ169bhrbfeanVMOFyTYw4cOIAffvgB1157bbvHheI1oVPjOBt6Im2c5Rir/bWJlHE2UsZYgONsbxEpYywQOeNspI2xAMdZra9PpIyxQOSMsxxjW/T6hLKqqpAkyf9YCNHq8em2h5rdu3fjhhtuwJ133tnqG5GoqCi8+OKL6N+/PxRFwQ033IAvv/xSu0BPYfTo0Xj88ccRExOD+Ph4zJ8/v1Ws4XZNAOD111/HT37ykzbPh9N1ATr2s2/vuVC9PuXl5bjuuutwySWXYMKECW22L1q0CGPHjoUkSbjpppvw9ddfaxDlqfXt2xeLFi1CUlISzGYzrrnmmja/Q+F0Td544w1ceeWVMBgM7W4Ph2tCrXGcDT2RNs5yjA3daxPu42ykjbEAx9lIE2ljLBD+42ykjbEAx9lQvT7hPsYCkTfO9rYxttcnlFNSUlBZWel/XFlZ2Wra+onbjxw50u609lCwfv16XH/99fjtb3+Liy66qNW20tLSVt/0CCH83/CEmnXr1mHNmjX+xyfGGk7XBADcbjfWrl2Lc845p822cLouwOn/XgAgPj4eDQ0N8Pl8J90nFOzduxcLFizARRddhF/84hdttjc0NOC///2v/7EQAjqdrgcj7JidO3fio48+8j9u73coXK4JAHz66aeYPXt2u9vC5ZpQaxxnQ08kjbMcY0P39TwSxtlIG2MBjrORJpLGWCAyxtlIGmMBjrOh+poeCWMsEHnjbG8bY3t9QnnixIlYs2YNqqur4XA4sGrVKkyZMsW/PT09HUajEevXrwcALFu2rNX2UFFWVoZf/OIXePLJJ3HBBRe02W4ymfDEE0/g0KFDEEJg8eLFmD59ugaRnl5DQwMef/xxuFwuNDY2YunSpa1iDZdrcszOnTuRlZUFi8XSZls4XRcAGDlyJPbv34+DBw/C5/Phvffea/Oz1+v1GDduHD744AMAwLvvvhty16exsRE33ngjfv3rX+OGG25odx+LxYJ//vOf/lWZX3nllZC8NkIIPProo6irq4PH48Ebb7zRJs5wuCZAyy1bTqcTffv2bXd7uFwTao3jbOiJpHGWY2xovp5HyjgbSWMswHE2EkXKGAtEzjgbSWMswHE2FF/TI2WMBSJrnO2VY2xQlvoLM8uXLxcXXHCBmDFjhvjHP/4hhBDipptuElu2bBFCCFFQUCAuueQSMXPmTPGb3/xGuFwuLcNt10MPPSRGjRol5s6d6//v1VdfbdWPlStX+vt51113hWQ/jnnqqafErFmzxIwZM8R///tfIUT4XZNj3n//fXHbbbe1ei7crsu0adP8q5euXr1a5OfnixkzZohHHnlEqKoqhBDinnvuEZ988okQQoji4mJx9dVXi/PPP1/ccMMNora2VrPYj3esH//5z3/E0KFDW/29PP3000KI1v1Yu3atuPDCC8WsWbPEzTffLOrr67UMv5Xjr8krr7wizj//fDF9+nTxxBNP+PcJp2sihBCbN28Wl156aZt9wuWa0MlxnA09kTLOcowNnddzISJnnI2UMVYIjrO9QSSMsUJE1jgbKWOsEBxnQ+k1PVLGWCEiZ5zt7WOsJIQQWie1iYiIiIiIiIiIiCj09fqSF0RERERERERERETUMUwoExEREREREREREVGHMKFMRERERERERERERB3ChDIRERERERERERERdQgTykRERERERERERETUIYrWARBRcAwePBiDBg2CLLf+3mjRokXo06dPwNtas2YN4uPjA3peIiKiUMVxloiIKDg4xhKFPiaUiSLYSy+9xIGRiIgoSDjOEhERBQfHWKLQxoQyUS/0/fff48knn0RaWhr27dsHk8mExx57DP3790dDQwMeeOABFBYWQpIknHXWWfjNb34DRVGwefNmPPzww3A4HNDr9bjzzjtx5plnAgCeffZZbN68GbW1tbjxxhtx1VVXadxLIiIibXCcJSIiCg6OsUShgQllogh23XXXtbpNqE+fPli0aBEAYNu2bfj973+PcePG4bXXXsMdd9yBd955Bw8//DCsVitWrFgBj8eDn//85/j3v/+Nn/zkJ/jFL36Bhx9+GFOnTsW2bdtw9913Y9myZQCAvn374v7778eOHTtw+eWX47LLLoNer9ek30RERD2B4ywREVFwcIwlCm1MKBNFsFPdJpSTk4Nx48YBAC655BI8+OCDqKmpwVdffYXXXnsNkiTBYDBgwYIFeOmllzBp0iTIsoypU6cCAIYNG4YVK1b4zzdnzhwAQG5uLtxuNxobG2Gz2YLbQSIiIg1xnCUiIgoOjrFEoU0+/S5EFIl0Ol27z6mqCkmS/M+pqgqv1wudTtfqeQDYtWsXvF4vAEBRWr6fOraPECJYoRMREYU8jrNERETBwTGWSHtMKBP1UoWFhSgsLAQAvPHGGxg9ejRiY2MxefJkvPLKKxBCwO12Y8mSJZg4cSL69esHSZLw7bffAgC2b9+O6667DqqqatkNIiKikMRxloiIKDg4xhJpjyUviCLYiXWnAOA3v/kNTCYTEhMT8fTTT6OkpATx8fF4/PHHAQD33XcfHn74YeTn58Pj8eCss87CzTffDIPBgGeffRaPPvooHn/8cej1ejz77LMwGAxadI2IiEhzHGeJiIiCg2MsUWiTBOfyE/U633//PR566CG89957WodCREQUcTjOEhERBQfHWKLQwJIXRERERERERERERNQhnKFMRERERERERERERB3CGcpERERERERERERE1CFMKBMRERERERERERFRhzChTEREREREREREREQdwoQyEREREREREREREXUIE8pERERERERERERE1CFMKBMRERERERERERFRh/x/Dt6XOVWyQ8QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x504 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_performance(loss=losses, error=errors, accuracy=accuracies, log_file=f'{log_dir}/hopfield_pooling.pdf')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
